diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 000000000..27be97f0f
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,2 @@
+[run]
+omit = replay/ann/index_stores/hdfs_index_store.py
\ No newline at end of file
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 7babe3f17..4550ec652 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -46,4 +46,4 @@ jobs:
       - name: pytest
         run: |
           . ./venv/bin/activate
-          pytest --cov=replay --cov-report=term-missing --doctest-modules replay --cov-fail-under=93 tests
+          pytest --cov-config=.coveragerc --cov=replay --cov-report=term-missing --doctest-modules replay --cov-fail-under=93 tests
diff --git a/poetry.lock b/poetry.lock
index 1ecb3fb3c..da8021740 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
@@ -14,14 +14,14 @@ files = [
 
 [[package]]
 name = "alembic"
-version = "1.9.3"
+version = "1.10.4"
 description = "A database migration tool for SQLAlchemy."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "alembic-1.9.3-py3-none-any.whl", hash = "sha256:ed2f73ea9c986f43af8ad7502c5f60d6bb1400bcd6d29f230e760e08884cb476"},
-    {file = "alembic-1.9.3.tar.gz", hash = "sha256:8fd6aaea56f5a703a190d25a705dfa91d7c313bb71de2f9c68f5abdcaf5df164"},
+    {file = "alembic-1.10.4-py3-none-any.whl", hash = "sha256:43942c3d4bf2620c466b91c0f4fca136fe51ae972394a0cc8b90810d664e4f5c"},
+    {file = "alembic-1.10.4.tar.gz", hash = "sha256:295b54bbb92c4008ab6a7dcd1e227e668416d6f84b98b3c4446a2bc6214a556b"},
 ]
 
 [package.dependencies]
@@ -29,6 +29,7 @@ importlib-metadata = {version = "*", markers = "python_version < \"3.9\""}
 importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 Mako = "*"
 SQLAlchemy = ">=1.3.0"
+typing-extensions = ">=4"
 
 [package.extras]
 tz = ["python-dateutil"]
@@ -170,22 +171,25 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "22.2.0"
+version = "23.1.0"
 description = "Classes Without Boilerplate"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"},
-    {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"},
+    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
+    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
 ]
 
+[package.dependencies]
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
+
 [package.extras]
-cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
-tests = ["attrs[tests-no-zope]", "zope.interface"]
-tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[docs,tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "autowoe"
@@ -218,18 +222,18 @@ tqdm = ">=4.62.3,<5.0.0"
 
 [[package]]
 name = "babel"
-version = "2.11.0"
+version = "2.12.1"
 description = "Internationalization utilities"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.11.0-py3-none-any.whl", hash = "sha256:1ad3eca1c885218f6dce2ab67291178944f810a10a9b5f3cb8382a5a232b64fe"},
-    {file = "Babel-2.11.0.tar.gz", hash = "sha256:5ef4b3226b0180dedded4229651c8b0e1a3a6a2837d45a073272f313e4cf97f6"},
+    {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"},
+    {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"},
 ]
 
 [package.dependencies]
-pytz = ">=2015.7"
+pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "backcall"
@@ -245,14 +249,14 @@ files = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.11.2"
+version = "4.12.2"
 description = "Screen-scraping library"
 category = "dev"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"},
-    {file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"},
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
 ]
 
 [package.dependencies]
@@ -264,37 +268,37 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "23.1.0"
+version = "23.3.0"
 description = "The uncompromising code formatter."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "black-23.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e6663f91b6feca5d06f2ccd49a10f254f9298cc1f7f49c46e498a0771b507104"},
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+    {file = "black-23.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a150542a204124ed00683f0db1f5cf1c2aaaa9cc3495b7a3b5976fb136090ab"},
+    {file = "black-23.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:6b39abdfb402002b8a7d030ccc85cf5afff64ee90fa4c5aebc531e3ad0175ddb"},
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+    {file = "black-23.3.0.tar.gz", hash = "sha256:1c7b8d606e728a41ea1ccbd7264677e494e87cf630e399262ced92d4a8dac940"},
 ]
 
 [package.dependencies]
@@ -332,50 +336,71 @@ webencodings = "*"
 [package.extras]
 css = ["tinycss2 (>=1.1.0,<1.2)"]
 
+[[package]]
+name = "cached-property"
+version = "1.5.2"
+description = "A decorator for caching properties in classes."
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "cached-property-1.5.2.tar.gz", hash = "sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130"},
+    {file = "cached_property-1.5.2-py2.py3-none-any.whl", hash = "sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0"},
+]
+
 [[package]]
 name = "catboost"
-version = "1.1.1"
-description = "Catboost Python Package"
+version = "1.2"
+description = "CatBoost Python Package"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 plotly = "*"
 scipy = "*"
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+
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 [package.extras]
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 [[package]]
@@ -857,14 +849,14 @@ files = [
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.2"
+version = "2.16.3"
 description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -872,19 +864,19 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
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 python-versions = ">=3.7"
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 [package.extras]
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 [[package]]
 name = "flake8"
@@ -1041,45 +1033,57 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "hijri-converter"
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+version = "2.3.1"
 description = "Accurate Hijri-Gregorian dates converter based on the Umm al-Qura calendar"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
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+
+[[package]]
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+optional = false
+python-versions = "*"
 files = [
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 ]
 
+[package.dependencies]
+numpy = "*"
+
 [[package]]
 name = "holidays"
-version = "0.19"
+version = "0.24"
 description = "Generate and work with holidays in Python"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
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 hijri-converter = "*"
 korean-lunar-calendar = "*"
-PyMeeus = "*"
 python-dateutil = "*"
 
 [[package]]
 name = "identify"
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+version = "2.5.24"
 description = "File identification library for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -1177,14 +1181,14 @@ testing = ["importlib-resources (>=1.3)", "packaging", "pep517"]
 
 [[package]]
 name = "importlib-resources"
-version = "5.10.2"
+version = "5.12.0"
 description = "Read resources from Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "importlib_resources-5.10.2-py3-none-any.whl", hash = "sha256:7d543798b0beca10b6a01ac7cafda9f822c54db9e8376a6bf57e0cbd74d486b6"},
-    {file = "importlib_resources-5.10.2.tar.gz", hash = "sha256:e4a96c8cc0339647ff9a5e0550d9f276fc5a01ffa276012b58ec108cfd7b8484"},
+    {file = "importlib_resources-5.12.0-py3-none-any.whl", hash = "sha256:7b1deeebbf351c7578e09bf2f63fa2ce8b5ffec296e0d349139d43cca061a81a"},
+    {file = "importlib_resources-5.12.0.tar.gz", hash = "sha256:4be82589bf5c1d7999aedf2a45159d10cb3ca4f19b2271f8792bc8e6da7b22f6"},
 ]
 
 [package.dependencies]
@@ -1274,25 +1278,25 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "8.0.4"
+version = "8.0.6"
 description = "Jupyter interactive widgets"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.0.4-py3-none-any.whl", hash = "sha256:ebb195e743b16c3947fe8827190fb87b4d00979c0fbf685afe4d2c4927059fa1"},
-    {file = "ipywidgets-8.0.4.tar.gz", hash = "sha256:c0005a77a47d77889cafed892b58e33b4a2a96712154404c6548ec22272811ea"},
+    {file = "ipywidgets-8.0.6-py3-none-any.whl", hash = "sha256:a60bf8d2528997e05ac83fd19ea2fbe65f2e79fbe1b2b35779bdfc46c2941dcc"},
+    {file = "ipywidgets-8.0.6.tar.gz", hash = "sha256:de7d779f2045d60de9f6c25f653fdae2dba57898e6a1284494b3ba20b6893bb8"},
 ]
 
 [package.dependencies]
 ipykernel = ">=4.5.1"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0,<4.0"
+jupyterlab-widgets = ">=3.0.7,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0,<5.0"
+widgetsnbextension = ">=4.0.7,<4.1.0"
 
 [package.extras]
-test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 
 [[package]]
 name = "isort"
@@ -1460,25 +1464,28 @@ test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-com
 
 [[package]]
 name = "jupyter-console"
-version = "6.4.4"
+version = "6.6.3"
 description = "Jupyter terminal console"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyter_console-6.4.4-py3-none-any.whl", hash = "sha256:756df7f4f60c986e7bc0172e4493d3830a7e6e75c08750bbe59c0a5403ad6dee"},
-    {file = "jupyter_console-6.4.4.tar.gz", hash = "sha256:172f5335e31d600df61613a97b7f0352f2c8250bbd1092ef2d658f77249f89fb"},
+    {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
+    {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
 ]
 
 [package.dependencies]
-ipykernel = "*"
+ipykernel = ">=6.14"
 ipython = "*"
 jupyter-client = ">=7.0.0"
-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+prompt-toolkit = ">=3.0.30"
 pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
 
 [package.extras]
-test = ["pexpect"]
+test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
@@ -1501,14 +1508,14 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-server"
-version = "1.23.5"
+version = "1.24.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyter_server-1.23.5-py3-none-any.whl", hash = "sha256:d42e520af98af9fbb7f0fb0d069991054d88e4d2ee051eb7110aef35f3756cef"},
-    {file = "jupyter_server-1.23.5.tar.gz", hash = "sha256:a2caa759e2a29ece1aad013c58215fe951f38317b97de462b921834955fe3fca"},
+    {file = "jupyter_server-1.24.0-py3-none-any.whl", hash = "sha256:c88ddbe862966ea1aea8c3ccb89a5903abd8fbcfe5cd14090ef549d403332c37"},
+    {file = "jupyter_server-1.24.0.tar.gz", hash = "sha256:23368e8e214baf82b313d4c5a0d828ca73015e1a192ce3829bd74e62fab8d046"},
 ]
 
 [package.dependencies]
@@ -1599,14 +1606,14 @@ test = ["codecov", "ipykernel", "jupyter-server[test]", "openapi-core (>=0.14.2)
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.5"
+version = "3.0.7"
 description = "Jupyter interactive widgets for JupyterLab"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_widgets-3.0.5-py3-none-any.whl", hash = "sha256:a04a42e50231b355b7087e16a818f541e53589f7647144ea0344c4bf16f300e5"},
-    {file = "jupyterlab_widgets-3.0.5.tar.gz", hash = "sha256:eeaecdeaf6c03afc960ddae201ced88d5979b4ca9c3891bcb8f6631af705f5ef"},
+    {file = "jupyterlab_widgets-3.0.7-py3-none-any.whl", hash = "sha256:c73f8370338ec19f1bec47254752d6505b03601cbd5a67e6a0b184532f73a459"},
+    {file = "jupyterlab_widgets-3.0.7.tar.gz", hash = "sha256:c3a50ed5bf528a0c7a869096503af54702f86dda1db469aee1c92dc0c01b43ca"},
 ]
 
 [[package]]
@@ -1793,13 +1800,13 @@ report = ["cffi (>=1.14.5,<2.0.0)", "weasyprint (>=52.5,<53.0)"]
 
 [[package]]
 name = "lightfm"
-version = "1.16"
+version = "1.17"
 description = "LightFM recommendation model"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "lightfm-1.16.tar.gz", hash = "sha256:41950fd8affde192c10b517148aa7f6d016ae2f75a2ec1187335f1c4d21876ac"},
+    {file = "lightfm-1.17.tar.gz", hash = "sha256:2b77ada182ccd768a8d7643ab3cfcd8b6e855db09087f7cc7329bd63316697a8"},
 ]
 
 [package.dependencies]
@@ -1981,14 +1988,14 @@ testing = ["pytest"]
 
 [[package]]
 name = "markdown-it-py"
-version = "2.1.0"
+version = "2.2.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "markdown-it-py-2.1.0.tar.gz", hash = "sha256:cf7e59fed14b5ae17c0006eff14a2d9a00ed5f3a846148153899a0224e2c07da"},
-    {file = "markdown_it_py-2.1.0-py3-none-any.whl", hash = "sha256:93de681e5c021a432c63147656fe21790bc01231e0cd2da73626f1aa3ac0fe27"},
+    {file = "markdown-it-py-2.2.0.tar.gz", hash = "sha256:7c9a5e412688bc771c67432cbfebcdd686c93ce6484913dccf06cb5a0bea35a1"},
+    {file = "markdown_it_py-2.2.0-py3-none-any.whl", hash = "sha256:5a35f8d1870171d9acc47b99612dc146129b631baf04970128b568f190d0cc30"},
 ]
 
 [package.dependencies]
@@ -1996,10 +2003,10 @@ mdurl = ">=0.1,<1.0"
 typing_extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
 
 [package.extras]
-benchmarking = ["psutil", "pytest", "pytest-benchmark (>=3.2,<4.0)"]
-code-style = ["pre-commit (==2.6)"]
-compare = ["commonmark (>=0.9.1,<0.10.0)", "markdown (>=3.3.6,<3.4.0)", "mistletoe (>=0.8.1,<0.9.0)", "mistune (>=2.0.2,<2.1.0)", "panflute (>=2.1.3,<2.2.0)"]
-linkify = ["linkify-it-py (>=1.0,<2.0)"]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
 plugins = ["mdit-py-plugins"]
 profiling = ["gprof2dot"]
 rtd = ["attrs", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
@@ -2119,7 +2126,6 @@ packaging = ">=20.0"
 pillow = ">=6.2.0"
 pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=4,<7"
 
 [[package]]
 name = "matplotlib-inline"
@@ -2150,14 +2156,14 @@ files = [
 
 [[package]]
 name = "mdit-py-plugins"
-version = "0.3.3"
+version = "0.3.5"
 description = "Collection of plugins for markdown-it-py"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "mdit-py-plugins-0.3.3.tar.gz", hash = "sha256:5cfd7e7ac582a594e23ba6546a2f406e94e42eb33ae596d0734781261c251260"},
-    {file = "mdit_py_plugins-0.3.3-py3-none-any.whl", hash = "sha256:36d08a29def19ec43acdcd8ba471d3ebab132e7879d442760d963f19913e04b9"},
+    {file = "mdit-py-plugins-0.3.5.tar.gz", hash = "sha256:eee0adc7195e5827e17e02d2a258a2ba159944a0748f59c5099a4a27f78fcf6a"},
+    {file = "mdit_py_plugins-0.3.5-py3-none-any.whl", hash = "sha256:ca9a0714ea59a24b2b044a1831f48d817dd0c817e84339f20e7889f392d77c4e"},
 ]
 
 [package.dependencies]
@@ -2194,14 +2200,14 @@ files = [
 
 [[package]]
 name = "more-itertools"
-version = "9.0.0"
+version = "9.1.0"
 description = "More routines for operating on iterables, beyond itertools"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "more-itertools-9.0.0.tar.gz", hash = "sha256:5a6257e40878ef0520b1803990e3e22303a41b5714006c32a3fd8304b26ea1ab"},
-    {file = "more_itertools-9.0.0-py3-none-any.whl", hash = "sha256:250e83d7e81d0c87ca6bd942e6aeab8cc9daa6096d12c5308f3f92fa5e5c1f41"},
+    {file = "more-itertools-9.1.0.tar.gz", hash = "sha256:cabaa341ad0389ea83c17a94566a53ae4c9d07349861ecb14dc6d0345cf9ac5d"},
+    {file = "more_itertools-9.1.0-py3-none-any.whl", hash = "sha256:d2bc7f02446e86a68911e58ded76d6561eea00cddfb2a91e7019bbb586c799f3"},
 ]
 
 [[package]]
@@ -2245,14 +2251,14 @@ testing = ["beautifulsoup4", "coverage[toml]", "pytest (>=6,<7)", "pytest-cov",
 
 [[package]]
 name = "nbclassic"
-version = "0.5.1"
+version = "1.0.0"
 description = "Jupyter Notebook as a Jupyter Server extension."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "nbclassic-0.5.1-py3-none-any.whl", hash = "sha256:32c235e1f22f4048f3b877d354c198202898797cf9c2085856827598cead001b"},
-    {file = "nbclassic-0.5.1.tar.gz", hash = "sha256:8e8ffce7582bb7a4baf11fa86a3d88b184e8e7df78eed4ead69f15aa4fc0e323"},
+    {file = "nbclassic-1.0.0-py3-none-any.whl", hash = "sha256:f99e4769b4750076cd4235c044b61232110733322384a94a63791d2e7beacc66"},
+    {file = "nbclassic-1.0.0.tar.gz", hash = "sha256:0ae11eb2319455d805596bf320336cda9554b41d99ab9a3c31bf8180bffa30e3"},
 ]
 
 [package.dependencies]
@@ -2262,11 +2268,11 @@ ipython-genutils = "*"
 jinja2 = "*"
 jupyter-client = ">=6.1.1"
 jupyter-core = ">=4.6.1"
-jupyter-server = ">=1.17.0"
+jupyter-server = ">=1.8"
 nbconvert = ">=5"
 nbformat = "*"
 nest-asyncio = ">=1.5"
-notebook-shim = ">=0.1.0"
+notebook-shim = ">=0.2.3"
 prometheus-client = "*"
 pyzmq = ">=17"
 Send2Trash = ">=1.8.0"
@@ -2281,14 +2287,14 @@ test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-p
 
 [[package]]
 name = "nbclient"
-version = "0.7.2"
+version = "0.7.4"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "dev"
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "nbclient-0.7.2-py3-none-any.whl", hash = "sha256:d97ac6257de2794f5397609df754fcbca1a603e94e924eb9b99787c031ae2e7c"},
-    {file = "nbclient-0.7.2.tar.gz", hash = "sha256:884a3f4a8c4fc24bb9302f263e0af47d97f0d01fe11ba714171b320c8ac09547"},
+    {file = "nbclient-0.7.4-py3-none-any.whl", hash = "sha256:c817c0768c5ff0d60e468e017613e6eae27b6fa31e43f905addd2d24df60c125"},
+    {file = "nbclient-0.7.4.tar.gz", hash = "sha256:d447f0e5a4cfe79d462459aec1b3dc5c2e9152597262be8ee27f7d4c02566a0d"},
 ]
 
 [package.dependencies]
@@ -2299,8 +2305,8 @@ traitlets = ">=5.3"
 
 [package.extras]
 dev = ["pre-commit"]
-docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme"]
-test = ["ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
@@ -2392,6 +2398,56 @@ doc = ["nb2plots (>=0.6)", "numpydoc (>=1.1)", "pillow (>=8.2)", "pydata-sphinx-
 extra = ["lxml (>=4.5)", "pydot (>=1.4.1)", "pygraphviz (>=1.7)"]
 test = ["codecov (>=2.1)", "pytest (>=6.2)", "pytest-cov (>=2.12)"]
 
+[[package]]
+name = "nmslib"
+version = "2.1.1"
+description = "Non-Metric Space Library (NMSLIB)"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "nmslib-2.1.1-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:495ace1146bb1e89ef585a490fa65de19a87fa0d5bb029f3156402a431fc3558"},
+    {file = "nmslib-2.1.1-cp27-cp27m-macosx_10_15_x86_64.whl", hash = "sha256:c0ae48f01e63e70dc1e45b28cb1b1478ca09588a21127f1d4473afb22ff3bbbc"},
+    {file = "nmslib-2.1.1-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:97b3f1af2c163369a043449dec7f05d681aa23c00f9d77bee09fd8f31ba41815"},
+    {file = "nmslib-2.1.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:2061e94f980666bffff4f2173bf2b58a763062dee9cedab11ec46001e576c6b5"},
+    {file = "nmslib-2.1.1-cp27-cp27m-win_amd64.whl", hash = "sha256:e8b30bab631258b36e751d217180cdba0d2f942d779d4444cbc57a5173ee19ad"},
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+psutil = "*"
+pybind11 = "<2.6.2"
+
 [[package]]
 name = "nodeenv"
 version = "1.7.0"
@@ -2409,14 +2465,14 @@ setuptools = "*"
 
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-version = "6.5.2"
+version = "6.5.4"
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 category = "dev"
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 python-versions = ">=3.7"
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+test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"]
 
 [[package]]
 name = "numba"
@@ -2547,14 +2603,14 @@ files = [
 
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 [[package]]
 name = "packaging"
-version = "23.0"
+version = "23.1"
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 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
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 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
@@ -2835,36 +2880,37 @@ files = [
 
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-version = "5.13.0"
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 description = "An open-source, interactive data visualization library for Python"
 category = "main"
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 [package.dependencies]
+packaging = "*"
 tenacity = ">=6.2.0"
 
 [[package]]
@@ -2887,14 +2933,14 @@ dev = ["pre-commit", "tox"]
 
 [[package]]
 name = "poetry-core"
-version = "1.5.0"
+version = "1.5.2"
 description = "Poetry PEP 517 Build Backend"
 category = "main"
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 [package.dependencies]
@@ -2938,14 +2984,14 @@ twisted = ["twisted"]
 
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+version = "3.0.38"
 description = "Library for building powerful interactive command lines in Python"
 category = "dev"
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 ]
 
 [package.dependencies]
@@ -2953,26 +2999,26 @@ wcwidth = "*"
 
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+version = "5.9.5"
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 category = "main"
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 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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 [package.extras]
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 ]
 
 [package.dependencies]
@@ -3516,14 +3566,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qtconsole"
-version = "5.4.0"
+version = "5.4.3"
 description = "Jupyter Qt console"
 category = "dev"
 optional = false
 python-versions = ">= 3.7"
 files = [
-    {file = "qtconsole-5.4.0-py3-none-any.whl", hash = "sha256:be13560c19bdb3b54ed9741a915aa701a68d424519e8341ac479a91209e694b2"},
-    {file = "qtconsole-5.4.0.tar.gz", hash = "sha256:57748ea2fd26320a0b77adba20131cfbb13818c7c96d83fafcb110ff55f58b35"},
+    {file = "qtconsole-5.4.3-py3-none-any.whl", hash = "sha256:35fd6e87b1f6d1fd41801b07e69339f8982e76afd4fa8ef35595bc6036717189"},
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 ]
 
 [package.dependencies]
@@ -3531,6 +3581,7 @@ ipykernel = ">=4.1"
 ipython-genutils = "*"
 jupyter-client = ">=4.1"
 jupyter-core = "*"
+packaging = "*"
 pygments = "*"
 pyzmq = ">=17.1"
 qtpy = ">=2.0.1"
@@ -3542,14 +3593,14 @@ test = ["flaky", "pytest", "pytest-qt"]
 
 [[package]]
 name = "qtpy"
-version = "2.3.0"
+version = "2.3.1"
 description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3560,21 +3611,21 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "requests"
-version = "2.28.2"
+version = "2.30.0"
 description = "Python HTTP for Humans."
 category = "main"
 optional = false
-python-versions = ">=3.7, <4"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 certifi = ">=2017.4.17"
 charset-normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<1.27"
+urllib3 = ">=1.21.1,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
@@ -3582,21 +3633,21 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rope"
-version = "1.7.0"
+version = "1.8.0"
 description = "a python refactoring library..."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 pytoolconfig = {version = ">=1.2.2", extras = ["global"]}
 
 [package.extras]
-dev = ["build (>=0.7.0)", "pre-commit (>=2.20.0)", "pytest (>=7.0.1)", "pytest-timeout (>=2.1.0)"]
+dev = ["build (>=0.7.0)", "pip-tools (>=6.12.1)", "pre-commit (>=2.20.0)", "pytest (>=7.0.1)", "pytest-timeout (>=2.1.0)", "toml (>=0.10.2)"]
 doc = ["pytoolconfig[doc]", "sphinx (>=4.5.0)", "sphinx-autodoc-typehints (>=1.18.1)", "sphinx-rtd-theme (>=1.0.0)"]
 
 [[package]]
@@ -3716,14 +3767,14 @@ stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
 
 [[package]]
 name = "send2trash"
-version = "1.8.0"
-description = "Send file to trash natively under Mac OS X, Windows and Linux."
+version = "1.8.2"
+description = "Send file to trash natively under Mac OS X, Windows and Linux"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 files = [
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 ]
 
 [package.extras]
@@ -3733,14 +3784,14 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "67.2.0"
+version = "67.7.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -3748,27 +3799,6 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-g
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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-[[package]]
-name = "setuptools-scm"
-version = "6.4.2"
-description = "the blessed package to manage your versions by scm tags"
-category = "main"
-optional = false
-python-versions = ">=3.6"
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-]
-
-[package.dependencies]
-packaging = ">=20.0"
-setuptools = "*"
-tomli = ">=1.0.0"
-
-[package.extras]
-test = ["pytest (>=6.2)", "virtualenv (>20)"]
-toml = ["setuptools (>=42)"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3807,14 +3837,14 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.3.2.post1"
+version = "2.4.1"
 description = "A modern CSS selector implementation for Beautiful Soup."
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -3942,18 +3972,18 @@ test = ["html5lib", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-jquery"
-version = "2.0.0"
+version = "4.1"
 description = "Extension to include jQuery on newer Sphinx releases"
 category = "main"
 optional = false
 python-versions = ">=2.7"
 files = [
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 ]
 
 [package.dependencies]
-setuptools = "*"
+Sphinx = ">=1.8"
 
 [[package]]
 name = "sphinxcontrib-jsmath"
@@ -4004,53 +4034,53 @@ test = ["pytest"]
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.2"
+version = "2.0.12"
 description = "Database Abstraction Library"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -4122,7 +4152,7 @@ packaging = ">=21.3"
 pandas = ">=0.25"
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 scipy = [
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+    {version = ">=1.3", markers = "python_version > \"3.9\" and python_version < \"3.12\" or platform_system != \"Windows\" and python_version < \"3.12\" or platform_machine != \"x86\" and python_version < \"3.12\""},
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 ]
@@ -4134,14 +4164,14 @@ docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "n
 
 [[package]]
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 [package.extras]
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 [[package]]
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 version = "2.0.1"
 description = "A lil' TOML parser"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -4403,32 +4433,33 @@ files = [
 
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-version = "1.26.14"
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 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
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-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
 
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 name = "virtualenv"
@@ -4496,107 +4527,118 @@ test = ["websockets"]
 
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 ]
 
 [[package]]
 name = "zipp"
-version = "3.12.1"
+version = "3.15.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "zipp-3.12.1-py3-none-any.whl", hash = "sha256:6c4fe274b8f85ec73c37a8e4e3fa00df9fb9335da96fb789e3b96b318e5097b3"},
-    {file = "zipp-3.12.1.tar.gz", hash = "sha256:a3cac813d40993596b39ea9e93a18e8a2076d5c378b8bc88ec32ab264e04ad02"},
+    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
+    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.7, <3.10"
-content-hash = "1b67e8140f9b46b8a368fd7da92fca532c79d765cfbb567ef51f9ee3d3366797"
+content-hash = "012c36aec17f0ed8f8864c6027f8e6f770cefe5ee9d0679e67fdaa8cdfd530ea"
diff --git a/pyproject.toml b/pyproject.toml
index f02f14e13..af3cc90ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = ">=3.7, <3.10"
+poetry-core = "1.6.0"
 lightfm = "*"
 lightautoml = ">=0.3.1, <0.3.7"
 numpy = ">=1.20.0"
@@ -46,6 +47,11 @@ seaborn = "*"
 pyarrow = "*"
 implicit = ">=0.5"
 pytorch-ranger = "^0.1.1"
+nmslib = "*"
+hnswlib = "*"
+cached-property = "*"
+# extra packages
+pygloo-rec = { version = "*", optional = true }
 
 [tool.poetry.dev-dependencies]
 # dev only
@@ -77,6 +83,9 @@ virtualenv = "*"
 data-science-types = "*"
 pyspark-stubs = "*"
 
+[tool.poetry.extras]
+distributed-lightfm = ["pygloo-rec"]
+
 [tool.black]
 line-length = 79
 
diff --git a/replay/ann/__init__.py b/replay/ann/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/replay/ann/ann_mixin.py b/replay/ann/ann_mixin.py
new file mode 100644
index 000000000..ac06baaea
--- /dev/null
+++ b/replay/ann/ann_mixin.py
@@ -0,0 +1,202 @@
+import importlib
+import logging
+from abc import abstractmethod
+from typing import Optional, Dict, Any
+
+from pyspark.sql import DataFrame
+from pyspark.sql import functions as sf
+
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.ann.index_stores.spark_files_index_store import (
+    SparkFilesIndexStore,
+)
+from replay.models.base_rec import BaseRecommender
+
+logger = logging.getLogger("replay")
+
+
+class ANNMixin(BaseRecommender):
+    """
+    This class overrides the `_fit_wrap` and `_inner_predict_wrap` methods of the base class,
+    adding an index construction in the `_fit_wrap` step
+    and an index inference in the `_inner_predict_wrap` step.
+    """
+
+    index_builder: Optional[IndexBuilder] = None
+
+    @property
+    def _use_ann(self) -> bool:
+        """
+        Property that determines whether the ANN (index) is used.
+        If `True`, then the index will be built (at the `fit` stage)
+        and index will be inferred (at the `predict` stage).
+        """
+        return self.index_builder is not None
+
+    @abstractmethod
+    def _get_vectors_to_build_ann(self, log: DataFrame) -> DataFrame:
+        """Implementations of this method must return a dataframe with item vectors.
+        Item vectors from this method are used to build the index.
+
+        Args:
+            log: DataFrame with interactions
+
+        Returns: DataFrame[item_idx int, vector array<double>] or DataFrame[vector array<double>].
+        Column names in dataframe can be anything.
+        """
+
+    @abstractmethod
+    def _get_ann_build_params(self, log: DataFrame) -> Dict[str, Any]:
+        """Implementation of this method must return dictionary
+        with arguments for `_build_ann_index` method.
+
+        Args:
+            log: DataFrame with interactions
+
+        Returns: Dictionary with arguments to build index. For example: {
+            "id_col": "item_idx",
+            "features_col": "item_factors",
+            ...
+        }
+
+        """
+
+    def _fit_wrap(
+        self,
+        log: DataFrame,
+        user_features: Optional[DataFrame] = None,
+        item_features: Optional[DataFrame] = None,
+    ) -> None:
+        """Wrapper extends `_fit_wrap`, adds construction of ANN index by flag.
+
+        Args:
+            log: historical log of interactions
+                ``[user_idx, item_idx, timestamp, relevance]``
+            user_features: user features
+                ``[user_idx, timestamp]`` + feature columns
+            item_features: item features
+                ``[item_idx, timestamp]`` + feature columns
+
+        """
+        super()._fit_wrap(log, user_features, item_features)
+
+        if self._use_ann:
+            vectors = self._get_vectors_to_build_ann(log)
+            ann_params = self._get_ann_build_params(log)
+            self.index_builder.build_index(vectors, **ann_params)
+
+    @abstractmethod
+    def _get_vectors_to_infer_ann_inner(
+        self, log: DataFrame, users: DataFrame
+    ) -> DataFrame:
+        """Implementations of this method must return a dataframe with user vectors.
+        User vectors from this method are used to infer the index.
+
+        Args:
+            log: DataFrame with interactions
+            users: DataFrame with users
+
+        Returns: DataFrame[user_idx int, vector array<double>] or DataFrame[vector array<double>].
+        Vector column name in dataframe can be anything.
+        """
+
+    def _get_vectors_to_infer_ann(
+        self, log: DataFrame, users: DataFrame, filter_seen_items: bool
+    ) -> DataFrame:
+        """This method wraps `_get_vectors_to_infer_ann_inner`
+        and adds seen items to dataframe with user vectors by flag.
+
+        Args:
+            log: DataFrame with interactions
+            users: DataFrame with users
+            filter_seen_items: flag to remove seen items from recommendations based on ``log``.
+
+        Returns:
+
+        """
+        users = self._get_vectors_to_infer_ann_inner(log, users)
+
+        # here we add `seen_item_idxs` to filter the viewed items in UDFs (see infer_index_udf)
+        if filter_seen_items:
+            user_to_max_items = log.groupBy("user_idx").agg(
+                sf.count("item_idx").alias("num_items"),
+                sf.collect_set("item_idx").alias("seen_item_idxs"),
+            )
+            users = users.join(user_to_max_items, on="user_idx")
+
+        return users
+
+    @abstractmethod
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        """Implementation of this method must return dictionary
+        with arguments for `_infer_ann_index` method.
+
+        Returns: Dictionary with arguments to infer index. For example: {
+            "features_col": "user_vector",
+            ...
+        }
+
+        """
+
+    def _inner_predict_wrap(  # pylint: disable=too-many-arguments
+        self,
+        log: DataFrame,
+        k: int,
+        users: DataFrame,
+        items: DataFrame,
+        user_features: Optional[DataFrame] = None,
+        item_features: Optional[DataFrame] = None,
+        filter_seen_items: bool = True,
+    ) -> DataFrame:
+        """Override base `_inner_predict_wrap` and adds ANN inference by condition"""
+        if self._use_ann:
+            vectors = self._get_vectors_to_infer_ann(
+                log, users, filter_seen_items
+            )
+            ann_params = self._get_ann_infer_params()
+            inferer = self.index_builder.produce_inferer(filter_seen_items)
+            return inferer.infer(vectors, ann_params["features_col"], k)
+        else:
+            return self._predict(
+                log,
+                k,
+                users,
+                items,
+                user_features,
+                item_features,
+                filter_seen_items,
+            )
+
+    def _filter_seen(
+        self, recs: DataFrame, log: DataFrame, k: int, users: DataFrame
+    ):
+        """
+        Overridden _filter_seen method from base class.
+        Filtering is not needed for ann methods, because the data is already filtered in udf.
+        """
+        if self._use_ann:
+            return recs
+
+        return super()._filter_seen(recs, log, k, users)
+
+    def _save_index(self, path):
+        self.index_builder.index_store.dump_index(path)
+
+    def _load_index(self, path: str):
+        self.index_builder.index_store = SparkFilesIndexStore()
+        self.index_builder.index_store.load_from_path(path)
+
+    def init_builder_from_dict(self, init_meta: dict):
+        """Inits an index builder instance from a dict with init meta."""
+
+        # index param entity instance initialization
+        module = importlib.import_module(init_meta["index_param"]["module"])
+        class_ = getattr(module, init_meta["index_param"]["class"])
+        index_params = class_(**init_meta["index_param"]["init_args"])
+
+        # index builder instance initialization
+        module = importlib.import_module(init_meta["builder"]["module"])
+        class_ = getattr(module, init_meta["builder"]["class"])
+        index_builder = class_(index_params=index_params, index_store=None)
+
+        self.index_builder = index_builder
diff --git a/replay/ann/entities/__init__.py b/replay/ann/entities/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/replay/ann/entities/base_hnsw_param.py b/replay/ann/entities/base_hnsw_param.py
new file mode 100644
index 000000000..2090596b6
--- /dev/null
+++ b/replay/ann/entities/base_hnsw_param.py
@@ -0,0 +1,32 @@
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class BaseHnswParam:
+    """
+    Base hnsw params.
+    """
+
+    space: str
+    m: int = 200  # pylint: disable=invalid-name
+    ef_c: int = 20000
+    post: int = 0
+    ef_s: Optional[int] = None
+
+    def init_meta_as_dict(self) -> dict:
+        """
+        Returns meta-information for class instance initialization. Used to save the entity to disk.
+        :return: dictionary with init meta.
+        """
+        return {
+            "module": type(self).__module__,
+            "class": type(self).__name__,
+            "init_args": {
+                "space": self.space,
+                "m": self.m,
+                "ef_c": self.ef_c,
+                "post": self.post,
+                "ef_s": self.ef_s,
+            },
+        }
diff --git a/replay/ann/entities/hnswlib_param.py b/replay/ann/entities/hnswlib_param.py
new file mode 100644
index 000000000..331186955
--- /dev/null
+++ b/replay/ann/entities/hnswlib_param.py
@@ -0,0 +1,66 @@
+from dataclasses import dataclass, field
+from typing_extensions import Literal
+
+from replay.ann.entities.base_hnsw_param import BaseHnswParam
+
+
+@dataclass
+class HnswlibParam(BaseHnswParam):
+    """
+    Parameters for hnswlib methods.
+
+    For example,
+
+    >>> HnswlibParam(space="ip",\
+                     m=100,\
+                     ef_c=200,\
+                     post=0,\
+                     ef_s=2000,\
+        )
+    HnswlibParam(space='ip', m=100, ef_c=200, post=0, ef_s=2000, dim=None, max_elements=None)
+
+    or
+
+    >>> HnswlibParam(space="ip",\
+                     m=100,\
+                     ef_c=200,\
+                     post=0,\
+                     ef_s=2000,\
+        )
+    HnswlibParam(space='ip', m=100, ef_c=200, post=0, ef_s=2000, dim=None, max_elements=None)
+
+    The "space" parameter described on the page https://github.com/nmslib/hnswlib/blob/master/README.md#supported-distances
+    Parameters "m", "ef_s" and "ef_c" are described at https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md
+
+    The reasonable range of values for `m` parameter is 5-100,
+    for `ef_c` and `ef_s` is 100-2000.
+    Increasing these values improves the prediction quality
+    but increases index_time and inference_time too.
+
+     We recommend using these settings:
+
+    - m=16, ef_c=200 and ef_s=200 for simple datasets like MovieLens.
+    - m=50, ef_c=1000 and ef_s=1000 for average quality with an average prediction time.
+    - m=75, ef_c=2000 and ef_s=2000 for the highest quality with a long prediction time.
+
+    note: choosing these parameters depends on the dataset
+    and quality/time tradeoff.
+
+    note: while reducing parameter values the highest range metrics
+    like Metric@1000 suffer first.
+
+    note: even in a case with a long training time,
+    profit from ann could be obtained while inference will be used multiple times.
+    """
+
+    space: Literal["l2", "ip", "cosine"] = "ip"
+    # Dimension of vectors in index
+    dim: int = field(default=None, init=False)
+    # Max number of elements that will be stored in the index
+    max_elements: int = field(default=None, init=False)
+
+    # def init_args_as_dict(self):
+    #     # union dicts
+    #     return dict(
+    #         super().init_args_as_dict()["init_args"], **{"space": self.space}
+    #     )
diff --git a/replay/ann/entities/nmslib_hnsw_param.py b/replay/ann/entities/nmslib_hnsw_param.py
new file mode 100644
index 000000000..9e852dbbb
--- /dev/null
+++ b/replay/ann/entities/nmslib_hnsw_param.py
@@ -0,0 +1,72 @@
+from dataclasses import dataclass, field
+from typing import ClassVar, Optional
+from typing_extensions import Literal
+
+from replay.ann.entities.base_hnsw_param import BaseHnswParam
+
+
+@dataclass
+class NmslibHnswParam(BaseHnswParam):
+    """
+    Parameters for nmslib-hnsw methods.
+
+    For example,
+
+    >>> NmslibHnswParam(space='negdotprod_sparse',\
+                        m=10,\
+                        ef_s=200,\
+                        ef_c=200,\
+                        post=0,\
+        )
+    NmslibHnswParam(space='negdotprod_sparse', m=10, ef_c=200, post=0, ef_s=200, items_count=None)
+
+    or
+
+    >>> NmslibHnswParam(space='negdotprod_sparse',\
+                        m=10,\
+                        ef_s=200,\
+                        ef_c=200,\
+                        post=0,\
+        )
+    NmslibHnswParam(space='negdotprod_sparse', m=10, ef_c=200, post=0, ef_s=200, items_count=None)
+
+    The reasonable range of values for `m` parameter is 5-100,
+    for `ef_c` and `ef_s` is 100-2000.
+    Increasing these values improves the prediction quality
+    but increases index_time and inference_time too.
+
+     We recommend using these settings:
+
+    - m=16, ef_c=200 and ef_s=200 for simple datasets like MovieLens.
+    - m=50, ef_c=1000 and ef_s=1000 for average quality with an average prediction time.
+    - m=75, ef_c=2000 and ef_s=2000 for the highest quality with a long prediction time.
+
+    note: choosing these parameters depends on the dataset
+    and quality/time tradeoff.
+
+    note: while reducing parameter values the highest range metrics
+    like Metric@1000 suffer first.
+
+    note: even in a case with a long training time,
+    profit from ann could be obtained while inference will be used multiple times.
+
+    For more details see https://github.com/nmslib/nmslib/blob/master/manual/methods.md.
+    """
+
+    space: Literal[
+        "cosinesimil_sparse",
+        "cosinesimil_sparse_fast",
+        "negdotprod_sparse",
+        "negdotprod_sparse_fast",
+        "angulardist_sparse",
+        "angulardist_sparse_fast",
+    ] = "negdotprod_sparse_fast"
+    items_count: Optional[int] = field(default=None, init=False)
+
+    method: ClassVar[str] = "hnsw"
+
+    # def init_args_as_dict(self):
+    #     # union dicts
+    #     return dict(
+    #         super().init_args_as_dict()["init_args"], **{"space": self.space}
+    #     )
diff --git a/replay/ann/index_builders/__init__.py b/replay/ann/index_builders/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/replay/ann/index_builders/base_index_builder.py b/replay/ann/index_builders/base_index_builder.py
new file mode 100644
index 000000000..048b80f65
--- /dev/null
+++ b/replay/ann/index_builders/base_index_builder.py
@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from pyspark.sql import DataFrame
+
+from replay.ann.entities.base_hnsw_param import BaseHnswParam
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_stores.base_index_store import IndexStore
+
+
+class IndexBuilder(ABC):
+    """Abstract base class for index builders.
+    Describes a common interface for index builders. And provides common methods for them.
+    """
+
+    def __init__(self, index_params: BaseHnswParam, index_store: IndexStore):
+        self.index_store = index_store
+        self.index_params = index_params
+
+    @abstractmethod
+    def build_index(
+        self,
+        vectors: DataFrame,
+        features_col: str,
+        ids_col: Optional[str] = None,
+    ):
+        """
+        Method that builds index and stores it using the `IndexStore` class.
+
+        :param vectors: DataFrame with vectors to build index.
+            Schema: [{ids_col}: int, {features_col}: array<float>]
+            or [{features_col}: array<float>]
+        :param features_col: Name of column from `vectors` dataframe
+            that contains vectors to build index
+        :param ids_col: Name of column that contains identifiers of vectors.
+            None if `vectors` dataframe have no id column.
+        """
+
+    @abstractmethod
+    def produce_inferer(self, filter_seen_items: bool) -> IndexInferer:
+        """Method that produce `IndexInferer`."""
+
+    def init_meta_as_dict(self):
+        """
+        Returns meta-information for class instance initialization.
+        Used to save the index builder to disk.
+        :return: dictionary with init meta.
+        """
+        return {
+            "builder": {
+                "module": type(self).__module__,
+                "class": type(self).__name__,
+            },
+            "index_param": self.index_params.init_meta_as_dict(),
+        }
diff --git a/replay/ann/index_builders/driver_hnswlib_index_builder.py b/replay/ann/index_builders/driver_hnswlib_index_builder.py
new file mode 100644
index 000000000..a2f600b4a
--- /dev/null
+++ b/replay/ann/index_builders/driver_hnswlib_index_builder.py
@@ -0,0 +1,51 @@
+import logging
+from typing import Optional
+
+import numpy as np
+from pyspark.sql import DataFrame
+
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.hnswlib_filter_index_inferer import (
+    HnswlibFilterIndexInferer,
+)
+from replay.ann.index_inferers.hnswlib_index_inferer import HnswlibIndexInferer
+from replay.ann.utils import create_hnswlib_index_instance
+
+logger = logging.getLogger("replay")
+
+
+class DriverHnswlibIndexBuilder(IndexBuilder):
+    """
+    Builder that builds hnswlib index on driver.
+    """
+
+    def produce_inferer(self, filter_seen_items: bool) -> IndexInferer:
+        if filter_seen_items:
+            return HnswlibFilterIndexInferer(
+                self.index_params, self.index_store
+            )
+        else:
+            return HnswlibIndexInferer(self.index_params, self.index_store)
+
+    def build_index(
+        self,
+        vectors: DataFrame,
+        features_col: str,
+        ids_col: Optional[str] = None,
+    ):
+        vectors = vectors.toPandas()
+        vectors_np = np.squeeze(vectors[features_col].values)
+
+        index = create_hnswlib_index_instance(self.index_params, init=True)
+
+        if ids_col:
+            index.add_items(np.stack(vectors_np), vectors[ids_col].values)
+        else:
+            index.add_items(np.stack(vectors_np))
+
+        self.index_store.save_to_store(
+            lambda path: index.save_index(  # pylint: disable=unnecessary-lambda)
+                path
+            )
+        )
diff --git a/replay/ann/index_builders/driver_nmslib_index_builder.py b/replay/ann/index_builders/driver_nmslib_index_builder.py
new file mode 100644
index 000000000..aacc4462f
--- /dev/null
+++ b/replay/ann/index_builders/driver_nmslib_index_builder.py
@@ -0,0 +1,65 @@
+import logging
+from typing import Optional
+
+from pyspark.sql import DataFrame
+from scipy.sparse import csr_matrix
+
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.nmslib_filter_index_inferer import (
+    NmslibFilterIndexInferer,
+)
+from replay.ann.index_inferers.nmslib_index_inferer import NmslibIndexInferer
+from replay.ann.utils import create_nmslib_index_instance
+
+logger = logging.getLogger("replay")
+
+
+class DriverNmslibIndexBuilder(IndexBuilder):
+    """
+    Builder that builds nmslib hnsw index on driver.
+    """
+
+    def produce_inferer(self, filter_seen_items: bool) -> IndexInferer:
+        if filter_seen_items:
+            return NmslibFilterIndexInferer(
+                self.index_params, self.index_store
+            )
+        else:
+            return NmslibIndexInferer(self.index_params, self.index_store)
+
+    def build_index(
+        self,
+        vectors: DataFrame,
+        features_col: str,
+        ids_col: Optional[str] = None,
+    ):
+        index_params = {
+            "M": self.index_params.m,
+            "efConstruction": self.index_params.ef_c,
+            "post": self.index_params.post,
+        }
+
+        vectors = vectors.toPandas()
+
+        index = create_nmslib_index_instance(self.index_params)
+
+        data = vectors["similarity"].values
+        row_ind = vectors["item_idx_two"].values
+        col_ind = vectors["item_idx_one"].values
+
+        sim_matrix = csr_matrix(
+            (data, (row_ind, col_ind)),
+            shape=(
+                self.index_params.items_count,
+                self.index_params.items_count,
+            ),
+        )
+        index.addDataPointBatch(data=sim_matrix)
+        index.createIndex(index_params)
+
+        self.index_store.save_to_store(
+            lambda path: index.saveIndex(
+                path, save_data=True
+            )  # pylint: disable=unnecessary-lambda)
+        )
diff --git a/replay/ann/index_builders/executor_hnswlib_index_builder.py b/replay/ann/index_builders/executor_hnswlib_index_builder.py
new file mode 100644
index 000000000..73ed086f3
--- /dev/null
+++ b/replay/ann/index_builders/executor_hnswlib_index_builder.py
@@ -0,0 +1,77 @@
+import logging
+from typing import Optional, Iterator
+
+import numpy as np
+import pandas as pd
+from pyspark.sql import DataFrame
+
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.hnswlib_filter_index_inferer import (
+    HnswlibFilterIndexInferer,
+)
+from replay.ann.index_inferers.hnswlib_index_inferer import HnswlibIndexInferer
+from replay.ann.utils import (
+    create_hnswlib_index_instance,
+)
+
+logger = logging.getLogger("replay")
+
+
+class ExecutorHnswlibIndexBuilder(IndexBuilder):
+    """
+    Builder that build hnswlib index on one executor.
+    """
+
+    def produce_inferer(self, filter_seen_items: bool) -> IndexInferer:
+        if filter_seen_items:
+            return HnswlibFilterIndexInferer(
+                self.index_params, self.index_store
+            )
+        else:
+            return HnswlibIndexInferer(self.index_params, self.index_store)
+
+    def build_index(
+        self,
+        vectors: DataFrame,
+        features_col: str,
+        ids_col: Optional[str] = None,
+    ):
+        # to execution in one executor
+        vectors = vectors.repartition(1)
+
+        _index_store = self.index_store
+        _index_params = self.index_params
+
+        def build_index_udf(iterator: Iterator[pd.DataFrame]):
+            """Builds index on executor and writes it to shared disk or hdfs.
+
+            Args:
+                iterator: iterates on dataframes with vectors/features
+
+            """
+            index = create_hnswlib_index_instance(_index_params, init=True)
+
+            # pdf is a pandas dataframe that contains ids and features (vectors)
+            for pdf in iterator:
+                vectors_np = np.squeeze(pdf[features_col].values)
+                if ids_col:
+                    index.add_items(np.stack(vectors_np), pdf[ids_col].values)
+                else:
+                    # ids will be from [0, ..., len(vectors_np)]
+                    index.add_items(np.stack(vectors_np))
+
+            _index_store.save_to_store(
+                lambda path: index.save_index(  # pylint: disable=unnecessary-lambda)
+                    path
+                )
+            )
+
+            yield pd.DataFrame(data={"_success": 1}, index=[0])
+
+        # Here we perform materialization (`.collect()`) to build the hnsw index.
+        cols = [ids_col, features_col] if ids_col else [features_col]
+
+        vectors.select(*cols).mapInPandas(
+            build_index_udf, "_success int"
+        ).collect()
diff --git a/replay/ann/index_builders/executor_nmslib_index_builder.py b/replay/ann/index_builders/executor_nmslib_index_builder.py
new file mode 100644
index 000000000..fe38bb610
--- /dev/null
+++ b/replay/ann/index_builders/executor_nmslib_index_builder.py
@@ -0,0 +1,94 @@
+import logging
+from typing import Optional, Iterator
+
+import pandas as pd
+from pyspark.sql import DataFrame
+from scipy.sparse import csr_matrix
+
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.nmslib_filter_index_inferer import (
+    NmslibFilterIndexInferer,
+)
+from replay.ann.index_inferers.nmslib_index_inferer import NmslibIndexInferer
+from replay.ann.utils import (
+    create_nmslib_index_instance,
+)
+
+logger = logging.getLogger("replay")
+
+
+class ExecutorNmslibIndexBuilder(IndexBuilder):
+    """
+    Builder that build nmslib hnsw index on one executor.
+    """
+
+    def produce_inferer(self, filter_seen_items: bool) -> IndexInferer:
+        if filter_seen_items:
+            return NmslibFilterIndexInferer(
+                self.index_params, self.index_store
+            )
+        else:
+            return NmslibIndexInferer(self.index_params, self.index_store)
+
+    def build_index(
+        self,
+        vectors: DataFrame,
+        features_col: str,
+        ids_col: Optional[str] = None,
+    ):
+        index_params = {
+            "M": self.index_params.m,
+            "efConstruction": self.index_params.ef_c,
+            "post": self.index_params.post,
+        }
+
+        # to execution in one executor
+        vectors = vectors.repartition(1)
+
+        _index_store = self.index_store
+        _index_params = self.index_params
+
+        def build_index_udf(iterator: Iterator[pd.DataFrame]):
+            """Builds index on executor and writes it to shared disk or hdfs.
+
+            Args:
+                iterator: iterates on dataframes with vectors/features
+
+            """
+            index = create_nmslib_index_instance(_index_params)
+
+            pdfs = []
+            for pdf in iterator:
+                pdfs.append(pdf)
+
+            pdf = pd.concat(pdfs, copy=False)
+
+            # We collect all iterator values into one dataframe,
+            # because we cannot guarantee that `pdf` will contain rows
+            # with the same `item_idx_two`.
+            # And therefore we cannot call the `addDataPointBatch` iteratively.
+            data = pdf["similarity"].values
+            row_ind = pdf["item_idx_two"].values
+            col_ind = pdf["item_idx_one"].values
+
+            sim_matrix_tmp = csr_matrix(
+                (data, (row_ind, col_ind)),
+                shape=(
+                    self.index_params.items_count,
+                    self.index_params.items_count,
+                ),
+            )
+            index.addDataPointBatch(data=sim_matrix_tmp)
+            index.createIndex(index_params)
+
+            _index_store.save_to_store(
+                lambda path: index.saveIndex(path, save_data=True)
+            )  # pylint: disable=unnecessary-lambda)
+
+            yield pd.DataFrame(data={"_success": 1}, index=[0])
+
+        # Here we perform materialization (`.collect()`) to build the hnsw index.
+        vectors.select(
+            "similarity", "item_idx_one", "item_idx_two"
+        ).mapInPandas(build_index_udf, "_success int").collect()
diff --git a/replay/ann/index_inferers/__init__.py b/replay/ann/index_inferers/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/replay/ann/index_inferers/base_inferer.py b/replay/ann/index_inferers/base_inferer.py
new file mode 100644
index 000000000..6b076a065
--- /dev/null
+++ b/replay/ann/index_inferers/base_inferer.py
@@ -0,0 +1,71 @@
+from abc import ABC, abstractmethod
+
+from pyspark.sql import DataFrame
+from pyspark.sql import functions as sf
+
+from replay.ann.entities.base_hnsw_param import BaseHnswParam
+from replay.ann.index_stores.base_index_store import IndexStore
+
+
+# pylint: disable=too-few-public-methods
+class IndexInferer(ABC):
+    """Abstract base class that describes a common interface for index inferers
+    and provides common methods for them."""
+
+    # All implementations use the same udf return type.
+    udf_return_type = "item_idx array<int>, distance array<double>"
+
+    def __init__(self, index_params: BaseHnswParam, index_store: IndexStore):
+        self.index_params = index_params
+        self.index_store = index_store
+
+    @abstractmethod
+    def infer(
+        self, vectors: DataFrame, features_col: str, k: int
+    ) -> DataFrame:
+        """Infers index"""
+
+    @staticmethod
+    def _unpack_infer_struct(inference_result: DataFrame) -> DataFrame:
+        """Transforms input dataframe.
+        Unpacks and explodes arrays from `neighbours` struct.
+
+        >>>
+        >> inference_result.printSchema()
+        root
+         |-- user_idx: integer (nullable = true)
+         |-- neighbours: struct (nullable = true)
+         |    |-- item_idx: array (nullable = true)
+         |    |    |-- element: integer (containsNull = true)
+         |    |-- distance: array (nullable = true)
+         |    |    |-- element: double (containsNull = true)
+        >> ANNMixin._unpack_infer_struct(inference_result).printSchema()
+        root
+         |-- user_idx: integer (nullable = true)
+         |-- item_idx: integer (nullable = true)
+         |-- relevance: double (nullable = true)
+
+        Args:
+            inference_result: output of infer_index UDF
+        """
+        res = inference_result.select(
+            "user_idx",
+            sf.explode(
+                sf.arrays_zip("neighbours.item_idx", "neighbours.distance")
+            ).alias("zip_exp"),
+        )
+
+        # Fix arrays_zip random behavior.
+        # It can return zip_exp.0 or zip_exp.item_idx in different machines.
+        fields = res.schema["zip_exp"].jsonValue()["type"]["fields"]
+        item_idx_field_name: str = fields[0]["name"]
+        distance_field_name: str = fields[1]["name"]
+
+        res = res.select(
+            "user_idx",
+            sf.col(f"zip_exp.{item_idx_field_name}").alias("item_idx"),
+            (sf.lit(-1.0) * sf.col(f"zip_exp.{distance_field_name}")).alias(
+                "relevance"
+            ),
+        )
+        return res
diff --git a/replay/ann/index_inferers/hnswlib_filter_index_inferer.py b/replay/ann/index_inferers/hnswlib_filter_index_inferer.py
new file mode 100644
index 000000000..ab8d3c5f6
--- /dev/null
+++ b/replay/ann/index_inferers/hnswlib_filter_index_inferer.py
@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+from pyspark.sql import DataFrame
+from pyspark.sql.pandas.functions import pandas_udf
+
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.utils import create_hnswlib_index_instance
+from replay.session_handler import State
+
+
+# pylint: disable=too-few-public-methods
+class HnswlibFilterIndexInferer(IndexInferer):
+    """Hnswlib index inferer with filter seen items. Infers hnswlib index."""
+
+    def infer(
+        self, vectors: DataFrame, features_col: str, k: int
+    ) -> DataFrame:
+        _index_store = self.index_store
+        index_params = self.index_params
+
+        index_store_broadcast = State().session.sparkContext.broadcast(
+            _index_store
+        )
+
+        @pandas_udf(self.udf_return_type)
+        def infer_index_udf(
+            vectors: pd.Series,
+            num_items: pd.Series,
+            seen_item_ids: pd.Series,
+        ) -> pd.DataFrame:
+            index_store = index_store_broadcast.value
+            index = index_store.load_index(
+                init_index=lambda: create_hnswlib_index_instance(index_params),
+                load_index=lambda index, path: index.load_index(path),
+                configure_index=lambda index: index.set_ef(index_params.ef_s)
+                if index_params.ef_s
+                else None,
+            )
+
+            # max number of items to retrieve per batch
+            max_items_to_retrieve = num_items.max()
+
+            labels, distances = index.knn_query(
+                np.stack(vectors.values),
+                k=k + max_items_to_retrieve,
+                num_threads=1,
+            )
+
+            filtered_labels = []
+            filtered_distances = []
+            for i, item_ids in enumerate(labels):
+                non_seen_item_indexes = ~np.isin(
+                    item_ids, seen_item_ids[i], assume_unique=True
+                )
+                filtered_labels.append((item_ids[non_seen_item_indexes])[:k])
+                filtered_distances.append(
+                    (distances[i][non_seen_item_indexes])[:k]
+                )
+
+            pd_res = pd.DataFrame(
+                {
+                    "item_idx": filtered_labels,
+                    "distance": filtered_distances,
+                }
+            )
+
+            return pd_res
+
+        cols = ["num_items", "seen_item_idxs"]
+
+        res = vectors.select(
+            "user_idx",
+            infer_index_udf(features_col, *cols).alias("neighbours"),
+        )
+        res = self._unpack_infer_struct(res)
+
+        return res
diff --git a/replay/ann/index_inferers/hnswlib_index_inferer.py b/replay/ann/index_inferers/hnswlib_index_inferer.py
new file mode 100644
index 000000000..e7f4e3eed
--- /dev/null
+++ b/replay/ann/index_inferers/hnswlib_index_inferer.py
@@ -0,0 +1,55 @@
+import numpy as np
+import pandas as pd
+from pyspark.sql import DataFrame
+from pyspark.sql.pandas.functions import pandas_udf
+
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.utils import create_hnswlib_index_instance
+from replay.session_handler import State
+
+
+# pylint: disable=too-few-public-methods
+class HnswlibIndexInferer(IndexInferer):
+    """Hnswlib index inferer without filter seen items. Infers hnswlib index."""
+
+    def infer(
+        self, vectors: DataFrame, features_col: str, k: int
+    ) -> DataFrame:
+        _index_store = self.index_store
+        index_params = self.index_params
+
+        index_store_broadcast = State().session.sparkContext.broadcast(
+            _index_store
+        )
+
+        @pandas_udf(self.udf_return_type)
+        def infer_index_udf(vectors: pd.Series) -> pd.DataFrame:
+            index_store = index_store_broadcast.value
+            index = index_store.load_index(
+                init_index=lambda: create_hnswlib_index_instance(index_params),
+                load_index=lambda index, path: index.load_index(path),
+                configure_index=lambda index: index.set_ef(index_params.ef_s)
+                if index_params.ef_s
+                else None,
+            )
+
+            labels, distances = index.knn_query(
+                np.stack(vectors.values),
+                k=k,
+                num_threads=1,
+            )
+
+            pd_res = pd.DataFrame(
+                {"item_idx": list(labels), "distance": list(distances)}
+            )
+
+            return pd_res
+
+        res = vectors.select(
+            "user_idx",
+            infer_index_udf(features_col).alias("neighbours"),
+        )
+
+        res = self._unpack_infer_struct(res)
+
+        return res
diff --git a/replay/ann/index_inferers/nmslib_filter_index_inferer.py b/replay/ann/index_inferers/nmslib_filter_index_inferer.py
new file mode 100644
index 000000000..7dd505f86
--- /dev/null
+++ b/replay/ann/index_inferers/nmslib_filter_index_inferer.py
@@ -0,0 +1,101 @@
+import numpy as np
+import pandas as pd
+from pyspark.sql import DataFrame
+from pyspark.sql.pandas.functions import pandas_udf
+
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.utils import (
+    get_csr_matrix,
+)
+from replay.ann.utils import create_nmslib_index_instance
+from replay.session_handler import State
+
+
+# pylint: disable=too-few-public-methods
+class NmslibFilterIndexInferer(IndexInferer):
+    """Nmslib index inferer with filter seen items. Infers nmslib hnsw index."""
+
+    def infer(
+        self, vectors: DataFrame, features_col: str, k: int
+    ) -> DataFrame:
+        _index_store = self.index_store
+        index_params = self.index_params
+
+        index_store_broadcast = State().session.sparkContext.broadcast(
+            _index_store
+        )
+
+        @pandas_udf(self.udf_return_type)
+        def infer_index_udf(  # pylint: disable=too-many-locals
+            user_idx: pd.Series,
+            vector_items: pd.Series,
+            vector_relevances: pd.Series,
+            num_items: pd.Series,
+            seen_item_ids: pd.Series,
+        ) -> pd.DataFrame:
+            index_store = index_store_broadcast.value
+            index = index_store.load_index(
+                init_index=lambda: create_nmslib_index_instance(index_params),
+                load_index=lambda index, path: index.loadIndex(
+                    path, load_data=True
+                ),
+                configure_index=lambda index: index.setQueryTimeParams(
+                    {"efSearch": index_params.ef_s}
+                )
+                if index_params.ef_s
+                else None,
+            )
+
+            # max number of items to retrieve per batch
+            max_items_to_retrieve = num_items.max()
+
+            user_vectors = get_csr_matrix(
+                user_idx, vector_items, vector_relevances
+            )
+
+            neighbours = index.knnQueryBatch(
+                user_vectors[user_idx.values, :],
+                k=k + max_items_to_retrieve,
+                num_threads=1,
+            )
+
+            neighbours_filtered = []
+            for i, (item_idxs, distances) in enumerate(neighbours):
+                non_seen_item_indexes = ~np.isin(
+                    item_idxs, seen_item_ids[i], assume_unique=True
+                )
+                neighbours_filtered.append(
+                    (
+                        (item_idxs[non_seen_item_indexes])[:k],
+                        (distances[non_seen_item_indexes])[:k],
+                    )
+                )
+
+            pd_res = pd.DataFrame(
+                neighbours_filtered, columns=["item_idx", "distance"]
+            )
+
+            # pd_res looks like
+            # item_idx       distances
+            # [1, 2, 3, ...] [-0.5, -0.3, -0.1, ...]
+            # [1, 3, 4, ...] [-0.1, -0.8, -0.2, ...]
+
+            return pd_res
+
+        cols = [
+            "user_idx",
+            "vector_items",
+            "vector_relevances",
+            "num_items",
+            "seen_item_idxs",
+        ]
+        # cols = cols + ["num_items", "seen_item_idxs"]
+
+        res = vectors.select(
+            "user_idx",
+            infer_index_udf(*cols).alias("neighbours"),
+        )
+
+        res = self._unpack_infer_struct(res)
+
+        return res
diff --git a/replay/ann/index_inferers/nmslib_index_inferer.py b/replay/ann/index_inferers/nmslib_index_inferer.py
new file mode 100644
index 000000000..cd1923b54
--- /dev/null
+++ b/replay/ann/index_inferers/nmslib_index_inferer.py
@@ -0,0 +1,71 @@
+import pandas as pd
+from pyspark.sql import DataFrame
+from pyspark.sql.pandas.functions import pandas_udf
+
+from replay.ann.index_inferers.base_inferer import IndexInferer
+from replay.ann.index_inferers.utils import (
+    get_csr_matrix,
+)
+from replay.ann.utils import create_nmslib_index_instance
+from replay.session_handler import State
+
+
+# pylint: disable=too-few-public-methods
+class NmslibIndexInferer(IndexInferer):
+    """Nmslib index inferer without filter seen items. Infers nmslib hnsw index."""
+
+    def infer(
+        self, vectors: DataFrame, features_col: str, k: int
+    ) -> DataFrame:
+        _index_store = self.index_store
+        index_params = self.index_params
+
+        index_store_broadcast = State().session.sparkContext.broadcast(
+            _index_store
+        )
+
+        @pandas_udf(self.udf_return_type)
+        def infer_index_udf(
+            user_idx: pd.Series,
+            vector_items: pd.Series,
+            vector_relevances: pd.Series,
+        ) -> pd.DataFrame:
+            index_store = index_store_broadcast.value
+            index = index_store.load_index(
+                init_index=lambda: create_nmslib_index_instance(index_params),
+                load_index=lambda index, path: index.loadIndex(
+                    path, load_data=True
+                ),
+                configure_index=lambda index: index.setQueryTimeParams(
+                    {"efSearch": index_params.ef_s}
+                )
+                if index_params.ef_s
+                else None,
+            )
+
+            user_vectors = get_csr_matrix(
+                user_idx, vector_items, vector_relevances
+            )
+            neighbours = index.knnQueryBatch(
+                user_vectors[user_idx.values, :], k=k, num_threads=1
+            )
+
+            pd_res = pd.DataFrame(neighbours, columns=["item_idx", "distance"])
+
+            # pd_res looks like
+            # item_idx       distances
+            # [1, 2, 3, ...] [-0.5, -0.3, -0.1, ...]
+            # [1, 3, 4, ...] [-0.1, -0.8, -0.2, ...]
+
+            return pd_res
+
+        cols = ["user_idx", "vector_items", "vector_relevances"]
+
+        res = vectors.select(
+            "user_idx",
+            infer_index_udf(*cols).alias("neighbours"),
+        )
+
+        res = self._unpack_infer_struct(res)
+
+        return res
diff --git a/replay/ann/index_inferers/utils.py b/replay/ann/index_inferers/utils.py
new file mode 100644
index 000000000..c74592598
--- /dev/null
+++ b/replay/ann/index_inferers/utils.py
@@ -0,0 +1,30 @@
+import pandas as pd
+from scipy.sparse import csr_matrix
+
+
+def get_csr_matrix(
+    user_idx: pd.Series,
+    vector_items: pd.Series,
+    vector_relevances: pd.Series,
+) -> csr_matrix:
+    """Creates and returns csr matrix of user-item interactions"""
+    return csr_matrix(
+        (
+            vector_relevances.explode().values.astype(float),
+            (
+                user_idx.repeat(
+                    vector_items.apply(
+                        lambda x: len(x)  # pylint: disable=unnecessary-lambda
+                    )
+                ).values,
+                vector_items.explode().values.astype(int),
+            ),
+        ),
+        shape=(
+            user_idx.max() + 1,
+            vector_items.apply(
+                lambda x: max(x)  # pylint: disable=unnecessary-lambda
+            ).max()
+            + 1,
+        ),
+    )
diff --git a/replay/ann/index_stores/__init__.py b/replay/ann/index_stores/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/replay/ann/index_stores/base_index_store.py b/replay/ann/index_stores/base_index_store.py
new file mode 100644
index 000000000..35ca24941
--- /dev/null
+++ b/replay/ann/index_stores/base_index_store.py
@@ -0,0 +1,29 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Any
+
+
+class IndexStore(ABC):
+    """Abstract base class for index stores. Describes a common interface for index stores."""
+
+    def __init__(self, cleanup: bool = True):
+        self._index = None
+        self.cleanup = cleanup
+
+    @abstractmethod
+    def save_to_store(self, save_index: Callable[[str], None]):
+        """Dumps index file to store"""
+
+    @abstractmethod
+    def load_index(
+        self,
+        init_index: Callable[[], None],
+        load_index: Callable[[Any, str], None],
+        configure_index: Callable[[Any], None],
+    ) -> Any:
+        """Loads index from IndexStore to index instance.
+        If the index has already been loaded before,
+        the previously loaded index instance is returned."""
+
+    @abstractmethod
+    def dump_index(self, target_path: str):
+        """Dumps index files to `target_path`"""
diff --git a/replay/ann/index_stores/hdfs_index_store.py b/replay/ann/index_stores/hdfs_index_store.py
new file mode 100644
index 000000000..d53ecf7ac
--- /dev/null
+++ b/replay/ann/index_stores/hdfs_index_store.py
@@ -0,0 +1,93 @@
+import logging
+import os
+import tempfile
+import weakref
+from typing import Callable, Any
+
+from pyarrow import fs
+
+from replay.ann.index_stores.base_index_store import IndexStore
+from replay.utils import get_filesystem, FileSystem
+
+logger = logging.getLogger("replay")
+
+
+class HdfsIndexStore(IndexStore):
+    """Class that responsible for index store in HDFS."""
+
+    def __init__(self, warehouse_dir: str, index_dir: str):
+        index_dir_path = os.path.join(warehouse_dir, index_dir)
+        self._index_dir_info = get_filesystem(index_dir_path)
+        if self._index_dir_info.filesystem != FileSystem.HDFS:
+            raise ValueError(
+                f"Can't recognize path {index_dir_path} as HDFS path!"
+            )
+        self._hadoop_fs = fs.HadoopFileSystem.from_uri(
+            self._index_dir_info.hdfs_uri
+        )
+        super().__init__()
+
+        if self.cleanup:
+            logger.debug(
+                "Index directory %s is marked for deletion via weakref.finalize()",
+                self._index_dir_info.path,
+            )
+            weakref.finalize(
+                self, self._hadoop_fs.delete_dir, self._index_dir_info.path
+            )
+
+    def load_index(
+        self,
+        init_index: Callable[[], None],
+        load_index: Callable[[Any, str], None],
+        configure_index: Callable[[Any], None],
+    ):
+        if self._index:
+            return self._index
+
+        self._index = init_index()
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file_path = os.path.join(temp_dir, "index")
+
+            # here we copy index files from hdfs directory
+            # to local disk directory
+            fs.copy_files(
+                self._index_dir_info.path,
+                "file://" + temp_dir,
+                source_filesystem=self._hadoop_fs,
+            )
+            load_index(self._index, temp_file_path)
+            configure_index(self._index)
+
+        return self._index
+
+    def save_to_store(self, save_index: Callable[[str], None]):
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file_path = os.path.join(temp_dir, "index")
+            save_index(temp_file_path)
+
+            # here we copy index files from local disk directory
+            # to hdfs directory
+            fs.copy_files(
+                "file://" + temp_dir,
+                self._index_dir_info.path,
+                destination_filesystem=self._hadoop_fs,
+            )
+            # param use_threads=True (?)
+            logger.info("Index files saved to %s", self._index_dir_info.path)
+
+    def dump_index(self, target_path: str):
+        target_path_info = get_filesystem(target_path)
+        destination_filesystem, target_path = fs.FileSystem.from_uri(
+            target_path_info.hdfs_uri + target_path_info.path
+            if target_path_info.filesystem == FileSystem.HDFS
+            else target_path_info.path
+        )
+        target_path = os.path.join(target_path, "index_files")
+        fs.copy_files(
+            self._index_dir_info.path,
+            target_path,
+            source_filesystem=self._hadoop_fs,
+            destination_filesystem=destination_filesystem,
+        )
diff --git a/replay/ann/index_stores/shared_disk_index_store.py b/replay/ann/index_stores/shared_disk_index_store.py
new file mode 100644
index 000000000..2cab6750e
--- /dev/null
+++ b/replay/ann/index_stores/shared_disk_index_store.py
@@ -0,0 +1,66 @@
+import logging
+import os
+import shutil
+import weakref
+from pathlib import Path
+from typing import Callable, Any
+
+from pyarrow import fs
+
+from replay.ann.index_stores.base_index_store import IndexStore
+
+
+logger = logging.getLogger("replay")
+
+
+class SharedDiskIndexStore(IndexStore):
+    """Class that responsible for index store in shared disk.
+    It can also be used with a local disk when the driver and executors
+    are running on the same machine."""
+
+    def __init__(
+        self, warehouse_dir: str, index_dir: str, cleanup: bool = True
+    ):
+        self.index_dir_path = os.path.join(warehouse_dir, index_dir)
+        super().__init__(cleanup)
+        if self.cleanup:
+            logger.debug(
+                "Index directory %s is marked for deletion via weakref.finalize()",
+                self.index_dir_path,
+            )
+            weakref.finalize(self, shutil.rmtree, self.index_dir_path)
+
+    def load_index(
+        self,
+        init_index: Callable[[], None],
+        load_index: Callable[[Any, str], None],
+        configure_index: Callable[[Any], None],
+    ):
+        if self._index:
+            return self._index
+
+        self._index = init_index()
+
+        temp_file_path = os.path.join(self.index_dir_path, "index")
+        load_index(self._index, temp_file_path)
+        configure_index(self._index)
+
+        return self._index
+
+    def save_to_store(self, save_index: Callable[[str], None]):
+        Path(self.index_dir_path).mkdir(parents=True, exist_ok=True)
+        temp_file_path = os.path.join(self.index_dir_path, "index")
+        save_index(temp_file_path)
+
+    def dump_index(self, target_path: str):
+        destination_filesystem, target_path = fs.FileSystem.from_uri(
+            target_path
+        )
+        target_path = os.path.join(target_path, "index_files")
+        destination_filesystem.create_dir(target_path)
+        fs.copy_files(
+            self.index_dir_path,
+            target_path,
+            source_filesystem=fs.LocalFileSystem(),
+            destination_filesystem=destination_filesystem,
+        )
diff --git a/replay/ann/index_stores/spark_files_index_store.py b/replay/ann/index_stores/spark_files_index_store.py
new file mode 100644
index 000000000..7db4f032c
--- /dev/null
+++ b/replay/ann/index_stores/spark_files_index_store.py
@@ -0,0 +1,101 @@
+import logging
+import os
+import shutil
+import tempfile
+import weakref
+from typing import Callable, Any
+
+from pyarrow import fs
+from pyspark import SparkFiles
+
+from replay.ann.index_stores.base_index_store import IndexStore
+from replay.session_handler import State
+from replay.utils import get_filesystem, FileSystem
+
+
+logger = logging.getLogger("replay")
+
+
+class SparkFilesIndexStore(IndexStore):
+    """Class that responsible for index store in spark files.
+    Works though SparkContext.addFile()."""
+
+    def _clean_up(self):
+        """Removes directory with index files
+        before the instance is garbage collected."""
+        if self.index_dir_path:
+            shutil.rmtree(self.index_dir_path)
+
+    def __init__(self, cleanup: bool = True):
+        self.index_dir_path = None
+        super().__init__(cleanup)
+        if self.cleanup:
+            weakref.finalize(self, self._clean_up)
+
+    def load_index(
+        self,
+        init_index: Callable[[], None],
+        load_index: Callable[[Any, str], None],
+        configure_index: Callable[[Any], None],
+    ):
+        if self._index:
+            return self._index
+
+        self._index = init_index()
+
+        temp_file_path = SparkFiles.get("index")
+        load_index(self._index, temp_file_path)
+        configure_index(self._index)
+
+        return self._index
+
+    def save_to_store(self, save_index: Callable[[str], None]):
+        self.index_dir_path: str = tempfile.mkdtemp()
+        temp_file_path = os.path.join(self.index_dir_path, "index")
+        save_index(temp_file_path)
+
+        spark = State().session
+        for filename in os.listdir(self.index_dir_path):
+            index_file_path = os.path.join(self.index_dir_path, filename)
+            spark.sparkContext.addFile("file://" + index_file_path)
+            logger.info(
+                "Index file %s transferred to executors", index_file_path
+            )
+
+    def dump_index(self, target_path: str):
+        destination_filesystem, target_path = fs.FileSystem.from_uri(
+            target_path
+        )
+        target_path = os.path.join(target_path, "index_files")
+        destination_filesystem.create_dir(target_path)
+        fs.copy_files(
+            self.index_dir_path,
+            target_path,
+            source_filesystem=fs.LocalFileSystem(),
+            destination_filesystem=destination_filesystem,
+        )
+
+    def load_from_path(self, path: str):
+        """Loads index from `path` directory to spark files."""
+        path_info = get_filesystem(path)
+        source_filesystem, path = fs.FileSystem.from_uri(
+            path_info.hdfs_uri + path_info.path
+            if path_info.filesystem == FileSystem.HDFS
+            else path_info.path
+        )
+        path = os.path.join(path, "index_files")
+        self.index_dir_path: str = tempfile.mkdtemp()
+        fs.copy_files(
+            path,
+            self.index_dir_path,
+            source_filesystem=source_filesystem,
+            destination_filesystem=fs.LocalFileSystem(),
+        )
+
+        spark = State().session
+        for filename in os.listdir(self.index_dir_path):
+            index_file_path = os.path.join(self.index_dir_path, filename)
+            spark.sparkContext.addFile("file://" + index_file_path)
+            logger.info(
+                "Index file %s transferred to executors", index_file_path
+            )
diff --git a/replay/ann/utils.py b/replay/ann/utils.py
new file mode 100644
index 000000000..37e57923d
--- /dev/null
+++ b/replay/ann/utils.py
@@ -0,0 +1,46 @@
+import hnswlib
+import nmslib
+
+from replay.ann.entities.hnswlib_param import HnswlibParam
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+
+
+def create_hnswlib_index_instance(params: HnswlibParam, init: bool = False):
+    """
+    Creates and returns hnswlib index
+
+    :param params: `HnswlibParam`
+    :param init: If `True` it will call the `init_index` method on the index.
+        Used when we want to create a new index.
+        If `False` then the index will be used to load index data from a file.
+    :return: `hnswlib` index instance
+    """
+    index = hnswlib.Index(  # pylint: disable=c-extension-no-member
+        space=params.space, dim=params.dim
+    )
+
+    if init:
+        # Initializing index - the maximum number of elements should be known beforehand
+        index.init_index(
+            max_elements=params.max_elements,
+            ef_construction=params.ef_c,
+            M=params.m,
+        )
+
+    return index
+
+
+def create_nmslib_index_instance(params: NmslibHnswParam):
+    """
+    Creates and returns nmslib index
+
+    :param params: `NmslibHnswParam`
+    :return: `nmslib` index
+    """
+    index = nmslib.init(  # pylint: disable=c-extension-no-member
+        method=params.method,
+        space=params.space,
+        data_type=nmslib.DataType.SPARSE_VECTOR,  # pylint: disable=c-extension-no-member
+    )
+
+    return index
diff --git a/replay/models/__init__.py b/replay/models/__init__.py
index 2d25953e4..31c238848 100644
--- a/replay/models/__init__.py
+++ b/replay/models/__init__.py
@@ -28,3 +28,4 @@
 from replay.models.cat_pop_rec import CatPopRec
 from replay.models.ucb import UCB
 from replay.models.thompson_sampling import ThompsonSampling
+from replay.models.distributed_lightfm_wrap.lightfm_wrap import DistributedLightFMWrap
diff --git a/replay/models/admm_slim.py b/replay/models/admm_slim.py
index 108566161..b7ba60cf3 100644
--- a/replay/models/admm_slim.py
+++ b/replay/models/admm_slim.py
@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Dict, Any
 
 import numba as nb
 import numpy as np
@@ -6,7 +6,8 @@
 from pyspark.sql import DataFrame
 from scipy.sparse import coo_matrix, csr_matrix
 
-from replay.models.base_rec import NeighbourRec
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+from replay.models.base_neighbour_rec import NeighbourRec
 from replay.session_handler import State
 
 
@@ -65,7 +66,7 @@ def _main_iteration(
     )
 
 
-# pylint: disable=too-many-instance-attributes
+# pylint: disable=too-many-instance-attributes, too-many-ancestors
 class ADMMSLIM(NeighbourRec):
     """`ADMM SLIM: Sparse Recommendations for Many Users
     <http://www.cs.columbia.edu/~jebara/papers/wsdm20_ADMM.pdf>`_
@@ -74,6 +75,12 @@ class ADMMSLIM(NeighbourRec):
     Recommendations are improved with Alternating Direction Method of Multipliers.
     """
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        return {
+            "features_col": None,
+            "params": self._nmslib_hnsw_params,
+        }
+
     rho: float
     threshold: float = 5
     multiplicator: float = 2
@@ -93,11 +100,14 @@ def __init__(
         lambda_1: float = 5,
         lambda_2: float = 5000,
         seed: Optional[int] = None,
+        nmslib_hnsw_params: Optional[NmslibHnswParam] = None,
     ):
         """
         :param lambda_1: l1 regularization term
         :param lambda_2: l2 regularization term
         :param seed: random seed
+        :param nmslib_hnsw_params: `NmslibHnswParam` instance, parameters for nmslib-hnsw methods.
+            If not set, then ann will not be used.
         """
         if lambda_1 < 0 or lambda_2 <= 0:
             raise ValueError("Invalid regularization parameters")
@@ -105,6 +115,7 @@ def __init__(
         self.lambda_2 = lambda_2
         self.rho = lambda_2
         self.seed = seed
+        self._nmslib_hnsw_params = nmslib_hnsw_params
 
     @property
     def _init_args(self):
diff --git a/replay/models/als.py b/replay/models/als.py
index 19a6c97cd..804ed8dd4 100644
--- a/replay/models/als.py
+++ b/replay/models/als.py
@@ -1,20 +1,47 @@
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Dict, Any
 
 import pyspark.sql.functions as sf
-
 from pyspark.ml.recommendation import ALS, ALSModel
 from pyspark.sql import DataFrame
 from pyspark.sql.types import DoubleType
 
+from replay.ann.ann_mixin import ANNMixin
+from replay.ann.index_builders.base_index_builder import IndexBuilder
 from replay.models.base_rec import Recommender, ItemVectorModel
 from replay.utils import list_to_vector_udf
 
 
-class ALSWrap(Recommender, ItemVectorModel):
+# pylint: disable=too-many-instance-attributes, too-many-ancestors
+class ALSWrap(Recommender, ItemVectorModel, ANNMixin):
     """Wrapper for `Spark ALS
     <https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.recommendation.ALS>`_.
     """
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        self.index_builder.index_params.dim = self.rank
+        return {
+            "features_col": "user_factors",
+            # "params": self._hnswlib_params,
+        }
+
+    def _get_vectors_to_infer_ann_inner(self, log: DataFrame, users: DataFrame) -> DataFrame:
+        user_vectors, _ = self.get_features(users)
+        return user_vectors
+
+    def _get_ann_build_params(self, log: DataFrame):
+        self.index_builder.index_params.dim = self.rank
+        self.index_builder.index_params.max_elements = log.select("item_idx").distinct().count()
+        return {
+            "features_col": "item_factors",
+            "ids_col": "item_idx",
+        }
+
+    def _get_vectors_to_build_ann(self, log: DataFrame) -> DataFrame:
+        item_vectors, _ = self.get_features(
+            log.select("item_idx").distinct()
+        )
+        return item_vectors
+
     _seed: Optional[int] = None
     _search_space = {
         "rank": {"type": "loguniform_int", "args": [8, 256]},
@@ -28,6 +55,7 @@ def __init__(
         seed: Optional[int] = None,
         num_item_blocks: Optional[int] = None,
         num_user_blocks: Optional[int] = None,
+        index_builder: Optional[IndexBuilder] = None,
     ):
         """
         :param rank: hidden dimension for the approximate matrix
@@ -45,6 +73,11 @@ def __init__(
         self._seed = seed
         self._num_item_blocks = num_item_blocks
         self._num_user_blocks = num_user_blocks
+        if isinstance(index_builder, (IndexBuilder, type(None))):
+            self.index_builder = index_builder
+        elif isinstance(index_builder, dict):
+            self.init_builder_from_dict(index_builder)
+        self.num_elements = None
 
     @property
     def _init_args(self):
@@ -52,16 +85,27 @@ def _init_args(self):
             "rank": self.rank,
             "implicit_prefs": self.implicit_prefs,
             "seed": self._seed,
+            "index_builder": self.index_builder.init_meta_as_dict() if self.index_builder else None,
         }
 
     def _save_model(self, path: str):
         self.model.write().overwrite().save(path)
 
+        # if self._hnswlib_params:
+        #     self._save_hnswlib_index(path)
+        if self._use_ann:
+            self._save_index(path)
+
     def _load_model(self, path: str):
         self.model = ALSModel.load(path)
         self.model.itemFactors.cache()
         self.model.userFactors.cache()
 
+        # if self._hnswlib_params:
+        #     self._load_hnswlib_index(path)
+        if self._use_ann:
+            self._load_index(path)
+
     def _fit(
         self,
         log: DataFrame,
diff --git a/replay/models/association_rules.py b/replay/models/association_rules.py
index ef328476f..9cb99d80d 100644
--- a/replay/models/association_rules.py
+++ b/replay/models/association_rules.py
@@ -1,4 +1,4 @@
-from typing import Iterable, List, Optional, Union
+from typing import Iterable, List, Optional, Union, Dict, Any
 
 import numpy as np
 
@@ -7,9 +7,11 @@
 from pyspark.sql import DataFrame
 from pyspark.sql.window import Window
 
-from replay.models.base_rec import NeighbourRec
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+from replay.models.base_neighbour_rec import NeighbourRec
 
 
+# pylint: disable=too-many-ancestors, too-many-instance-attributes
 class AssociationRulesItemRec(NeighbourRec):
     """
     Item-to-item recommender based on association rules.
@@ -70,6 +72,12 @@ class AssociationRulesItemRec(NeighbourRec):
     In this case all items in sessions should have the same relevance.
     """
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        return {
+            "features_col": None,
+            "params": self._nmslib_hnsw_params,
+        }
+
     can_predict_item_to_item = True
     item_to_item_metrics: List[str] = ["lift", "confidence", "confidence_gain"]
     similarity: DataFrame
@@ -94,6 +102,7 @@ def __init__(
         num_neighbours: Optional[int] = 1000,
         use_relevance: bool = False,
         similarity_metric: str = "confidence",
+        nmslib_hnsw_params: Optional[NmslibHnswParam] = None,
     ) -> None:
         """
         :param session_col: name of column to group sessions.
@@ -107,6 +116,8 @@ def __init__(
         :param similarity_metric: `lift` of 'confidence'
             The metric used as a similarity to calculate the prediction,
             one of [``lift``, ``confidence``, ``confidence_gain``]
+        :param nmslib_hnsw_params: `NmslibHnswParam` instance, parameters for nmslib-hnsw methods.
+            If not set, then ann will not be used.
         """
 
         self.session_col = (
@@ -117,6 +128,7 @@ def __init__(
         self.num_neighbours = num_neighbours
         self.use_relevance = use_relevance
         self.similarity_metric = similarity_metric
+        self._nmslib_hnsw_params = nmslib_hnsw_params
 
     @property
     def _init_args(self):
diff --git a/replay/models/base_neighbour_rec.py b/replay/models/base_neighbour_rec.py
new file mode 100644
index 000000000..8c85ee555
--- /dev/null
+++ b/replay/models/base_neighbour_rec.py
@@ -0,0 +1,227 @@
+# pylint: disable=too-many-lines
+"""
+NeighbourRec - base class that requires log at prediction time.
+Part of set of abstract classes (from base_rec.py)
+"""
+
+from abc import ABC
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    Optional,
+    Union,
+)
+
+from pyspark.sql import DataFrame
+from pyspark.sql import functions as sf
+from pyspark.sql.column import Column
+
+from replay.ann.ann_mixin import ANNMixin
+from replay.models.base_rec import Recommender
+
+
+class NeighbourRec(Recommender, ANNMixin, ABC):
+    """Base class that requires log at prediction time"""
+
+    similarity: Optional[DataFrame]
+    can_predict_item_to_item: bool = True
+    can_predict_cold_users: bool = True
+    can_change_metric: bool = False
+    item_to_item_metrics = ["similarity"]
+    _similarity_metric = "similarity"
+
+    @property
+    def _dataframes(self):
+        return {"similarity": self.similarity}
+
+    def _clear_cache(self):
+        if hasattr(self, "similarity"):
+            self.similarity.unpersist()
+
+    # pylint: disable=missing-function-docstring
+    @property
+    def similarity_metric(self):
+        return self._similarity_metric
+
+    @similarity_metric.setter
+    def similarity_metric(self, value):
+        if not self.can_change_metric:
+            raise ValueError(
+                "This class does not support changing similarity metrics"
+            )
+        if value not in self.item_to_item_metrics:
+            raise ValueError(
+                f"Select one of the valid metrics for predict: "
+                f"{self.item_to_item_metrics}"
+            )
+        self._similarity_metric = value
+
+    def _predict_pairs_inner(
+        self,
+        log: DataFrame,
+        filter_df: DataFrame,
+        condition: Column,
+        users: DataFrame,
+    ) -> DataFrame:
+        """
+        Get recommendations for all provided users
+        and filter results with ``filter_df`` by ``condition``.
+        It allows to implement both ``predict_pairs`` and usual ``predict``@k.
+
+        :param log: historical interactions, DataFrame
+            ``[user_idx, item_idx, timestamp, relevance]``.
+        :param filter_df: DataFrame use to filter items:
+            ``[item_idx_filter]`` or ``[user_idx_filter, item_idx_filter]``.
+        :param condition: condition used for inner join with ``filter_df``
+        :param users: users to calculate recommendations for
+        :return: DataFrame ``[user_idx, item_idx, relevance]``
+        """
+        if log is None:
+            raise ValueError(
+                "log is not provided, but it is required for prediction"
+            )
+
+        recs = (
+            log.join(users, how="inner", on="user_idx")
+            .join(
+                self.similarity,
+                how="inner",
+                on=sf.col("item_idx") == sf.col("item_idx_one"),
+            )
+            .join(
+                filter_df,
+                how="inner",
+                on=condition,
+            )
+            .groupby("user_idx", "item_idx_two")
+            .agg(sf.sum(self.similarity_metric).alias("relevance"))
+            .withColumnRenamed("item_idx_two", "item_idx")
+        )
+        return recs
+
+    # pylint: disable=too-many-arguments
+    def _predict(
+        self,
+        log: DataFrame,
+        k: int,
+        users: DataFrame,
+        items: DataFrame,
+        user_features: Optional[DataFrame] = None,
+        item_features: Optional[DataFrame] = None,
+        filter_seen_items: bool = True,
+    ) -> DataFrame:
+
+        return self._predict_pairs_inner(
+            log=log,
+            filter_df=items.withColumnRenamed("item_idx", "item_idx_filter"),
+            condition=sf.col("item_idx_two") == sf.col("item_idx_filter"),
+            users=users,
+        )
+
+    def _predict_pairs(
+        self,
+        pairs: DataFrame,
+        log: Optional[DataFrame] = None,
+        user_features: Optional[DataFrame] = None,
+        item_features: Optional[DataFrame] = None,
+    ) -> DataFrame:
+
+        if log is None:
+            raise ValueError(
+                "log is not provided, but it is required for prediction"
+            )
+
+        return self._predict_pairs_inner(
+            log=log,
+            filter_df=(
+                pairs.withColumnRenamed(
+                    "user_idx", "user_idx_filter"
+                ).withColumnRenamed("item_idx", "item_idx_filter")
+            ),
+            condition=(sf.col("user_idx") == sf.col("user_idx_filter"))
+            & (sf.col("item_idx_two") == sf.col("item_idx_filter")),
+            users=pairs.select("user_idx").distinct(),
+        )
+
+    def get_nearest_items(
+        self,
+        items: Union[DataFrame, Iterable],
+        k: int,
+        metric: Optional[str] = None,
+        candidates: Optional[Union[DataFrame, Iterable]] = None,
+    ) -> DataFrame:
+        """
+        Get k most similar items be the `metric` for each of the `items`.
+
+        :param items: spark dataframe or list of item ids to find neighbors
+        :param k: number of neighbors
+        :param metric: metric is not used to find neighbours in NeighbourRec,
+            the parameter is ignored
+        :param candidates: spark dataframe or list of items
+            to consider as similar, e.g. popular/new items. If None,
+            all items presented during model training are used.
+        :return: dataframe with the most similar items an distance,
+            where bigger value means greater similarity.
+            spark-dataframe with columns ``[item_idx, neighbour_item_idx, similarity]``
+        """
+
+        if metric is not None:
+            self.logger.debug(
+                "Metric is not used to determine nearest items in %s model",
+                str(self),
+            )
+
+        return self._get_nearest_items_wrap(
+            items=items,
+            k=k,
+            metric=metric,
+            candidates=candidates,
+        )
+
+    def _get_nearest_items(
+        self,
+        items: DataFrame,
+        metric: Optional[str] = None,
+        candidates: Optional[DataFrame] = None,
+    ) -> DataFrame:
+
+        similarity_filtered = self.similarity.join(
+            items.withColumnRenamed("item_idx", "item_idx_one"),
+            on="item_idx_one",
+        )
+
+        if candidates is not None:
+            similarity_filtered = similarity_filtered.join(
+                candidates.withColumnRenamed("item_idx", "item_idx_two"),
+                on="item_idx_two",
+            )
+
+        return similarity_filtered.select(
+            "item_idx_one",
+            "item_idx_two",
+            "similarity" if metric is None else metric,
+        )
+
+    def _get_ann_build_params(self, log: DataFrame) -> Dict[str, Any]:
+        self.index_builder.index_params.items_count = log.select(sf.max("item_idx")).first()[0] + 1
+        return {
+            "features_col": None,
+        }
+
+    def _get_vectors_to_build_ann(self, log: DataFrame) -> DataFrame:
+        similarity_df = self.similarity.select(
+            "similarity", "item_idx_one", "item_idx_two"
+        )
+        return similarity_df
+
+    def _get_vectors_to_infer_ann_inner(
+            self, log: DataFrame, users: DataFrame
+    ) -> DataFrame:
+
+        user_vectors = (
+            log.groupBy("user_idx").agg(
+                sf.collect_list("item_idx").alias("vector_items"),
+                sf.collect_list("relevance").alias("vector_relevances"))
+        )
+        return user_vectors
diff --git a/replay/models/base_rec.py b/replay/models/base_rec.py
index 8be7fa625..c0e8f4b8b 100644
--- a/replay/models/base_rec.py
+++ b/replay/models/base_rec.py
@@ -544,7 +544,7 @@ def _predict_wrap(
             message = f"k = {k} > number of items = {num_items}"
             self.logger.debug(message)
 
-        recs = self._predict(
+        recs = self._inner_predict_wrap(
             log,
             k,
             users,
@@ -630,6 +630,48 @@ def _predict(
             ``[user_idx, item_idx, relevance]``
         """
 
+    def _inner_predict_wrap(
+        self,
+        log: DataFrame,
+        k: int,
+        users: DataFrame,
+        items: DataFrame,
+        user_features: Optional[DataFrame] = None,
+        item_features: Optional[DataFrame] = None,
+        filter_seen_items: bool = True,
+    ) -> DataFrame:
+        """
+        Inner method that wrap _predict method. Can be overwritten.
+
+        :param log: historical log of interactions
+            ``[user_idx, item_idx, timestamp, relevance]``
+        :param k: number of recommendations for each user
+        :param users: users to create recommendations for
+            dataframe containing ``[user_idx]`` or ``array-like``;
+            if ``None``, recommend to all users from ``log``
+        :param items: candidate items for recommendations
+            dataframe containing ``[item_idx]`` or ``array-like``;
+            if ``None``, take all items from ``log``.
+            If it contains new items, ``relevance`` for them will be ``0``.
+        :param user_features: user features
+            ``[user_idx , timestamp]`` + feature columns
+        :param item_features: item features
+            ``[item_idx , timestamp]`` + feature columns
+        :param filter_seen_items: flag to remove seen items from recommendations based on ``log``.
+        :return: recommendation dataframe
+            ``[user_idx, item_idx, relevance]``
+        """
+
+        return self._predict(
+            log,
+            k,
+            users,
+            items,
+            user_features,
+            item_features,
+            filter_seen_items,
+        )
+
     def _get_fit_counts(self, entity: str) -> int:
         if not hasattr(self, f"_num_{entity}s"):
             setattr(
@@ -1405,189 +1447,6 @@ def predict_pairs(
         )
 
 
-class NeighbourRec(Recommender, ABC):
-    """Base class that requires log at prediction time"""
-
-    similarity: Optional[DataFrame]
-    can_predict_item_to_item: bool = True
-    can_predict_cold_users: bool = True
-    can_change_metric: bool = False
-    item_to_item_metrics = ["similarity"]
-    _similarity_metric = "similarity"
-
-    @property
-    def _dataframes(self):
-        return {"similarity": self.similarity}
-
-    def _clear_cache(self):
-        if hasattr(self, "similarity"):
-            self.similarity.unpersist()
-
-    # pylint: disable=missing-function-docstring
-    @property
-    def similarity_metric(self):
-        return self._similarity_metric
-
-    @similarity_metric.setter
-    def similarity_metric(self, value):
-        if not self.can_change_metric:
-            raise ValueError(
-                "This class does not support changing similarity metrics"
-            )
-        if value not in self.item_to_item_metrics:
-            raise ValueError(
-                f"Select one of the valid metrics for predict: "
-                f"{self.item_to_item_metrics}"
-            )
-        self._similarity_metric = value
-
-    def _predict_pairs_inner(
-        self,
-        log: DataFrame,
-        filter_df: DataFrame,
-        condition: Column,
-        users: DataFrame,
-    ) -> DataFrame:
-        """
-        Get recommendations for all provided users
-        and filter results with ``filter_df`` by ``condition``.
-        It allows to implement both ``predict_pairs`` and usual ``predict``@k.
-
-        :param log: historical interactions, DataFrame
-            ``[user_idx, item_idx, timestamp, relevance]``.
-        :param filter_df: DataFrame use to filter items:
-            ``[item_idx_filter]`` or ``[user_idx_filter, item_idx_filter]``.
-        :param condition: condition used for inner join with ``filter_df``
-        :param users: users to calculate recommendations for
-        :return: DataFrame ``[user_idx, item_idx, relevance]``
-        """
-        if log is None:
-            raise ValueError(
-                "log is not provided, but it is required for prediction"
-            )
-
-        recs = (
-            log.join(users, how="inner", on="user_idx")
-            .join(
-                self.similarity,
-                how="inner",
-                on=sf.col("item_idx") == sf.col("item_idx_one"),
-            )
-            .join(
-                filter_df,
-                how="inner",
-                on=condition,
-            )
-            .groupby("user_idx", "item_idx_two")
-            .agg(sf.sum(self.similarity_metric).alias("relevance"))
-            .withColumnRenamed("item_idx_two", "item_idx")
-        )
-        return recs
-
-    # pylint: disable=too-many-arguments
-    def _predict(
-        self,
-        log: DataFrame,
-        k: int,
-        users: DataFrame,
-        items: DataFrame,
-        user_features: Optional[DataFrame] = None,
-        item_features: Optional[DataFrame] = None,
-        filter_seen_items: bool = True,
-    ) -> DataFrame:
-
-        return self._predict_pairs_inner(
-            log=log,
-            filter_df=items.withColumnRenamed("item_idx", "item_idx_filter"),
-            condition=sf.col("item_idx_two") == sf.col("item_idx_filter"),
-            users=users,
-        )
-
-    def _predict_pairs(
-        self,
-        pairs: DataFrame,
-        log: Optional[DataFrame] = None,
-        user_features: Optional[DataFrame] = None,
-        item_features: Optional[DataFrame] = None,
-    ) -> DataFrame:
-
-        if log is None:
-            raise ValueError(
-                "log is not provided, but it is required for prediction"
-            )
-
-        return self._predict_pairs_inner(
-            log=log,
-            filter_df=(
-                pairs.withColumnRenamed(
-                    "user_idx", "user_idx_filter"
-                ).withColumnRenamed("item_idx", "item_idx_filter")
-            ),
-            condition=(sf.col("user_idx") == sf.col("user_idx_filter"))
-            & (sf.col("item_idx_two") == sf.col("item_idx_filter")),
-            users=pairs.select("user_idx").distinct(),
-        )
-
-    def get_nearest_items(
-        self,
-        items: Union[DataFrame, Iterable],
-        k: int,
-        metric: Optional[str] = None,
-        candidates: Optional[Union[DataFrame, Iterable]] = None,
-    ) -> DataFrame:
-        """
-        Get k most similar items be the `metric` for each of the `items`.
-
-        :param items: spark dataframe or list of item ids to find neighbors
-        :param k: number of neighbors
-        :param metric: metric is not used to find neighbours in NeighbourRec,
-            the parameter is ignored
-        :param candidates: spark dataframe or list of items
-            to consider as similar, e.g. popular/new items. If None,
-            all items presented during model training are used.
-        :return: dataframe with the most similar items an distance,
-            where bigger value means greater similarity.
-            spark-dataframe with columns ``[item_idx, neighbour_item_idx, similarity]``
-        """
-
-        if metric is not None:
-            self.logger.debug(
-                "Metric is not used to determine nearest items in %s model",
-                str(self),
-            )
-
-        return self._get_nearest_items_wrap(
-            items=items,
-            k=k,
-            metric=metric,
-            candidates=candidates,
-        )
-
-    def _get_nearest_items(
-        self,
-        items: DataFrame,
-        metric: Optional[str] = None,
-        candidates: Optional[DataFrame] = None,
-    ) -> DataFrame:
-
-        similarity_filtered = self.similarity.join(
-            items.withColumnRenamed("item_idx", "item_idx_one"),
-            on="item_idx_one",
-        )
-
-        if candidates is not None:
-            similarity_filtered = similarity_filtered.join(
-                candidates.withColumnRenamed("item_idx", "item_idx_two"),
-                on="item_idx_two",
-            )
-
-        return similarity_filtered.select(
-            "item_idx_one",
-            "item_idx_two",
-            "similarity" if metric is None else metric,
-        )
-
-
 class NonPersonalizedRecommender(Recommender, ABC):
     """Base class for non-personalized recommenders with popularity statistics."""
 
diff --git a/replay/models/distributed_lightfm_wrap/lightfm_wrap.py b/replay/models/distributed_lightfm_wrap/lightfm_wrap.py
new file mode 100644
index 000000000..3851cadc9
--- /dev/null
+++ b/replay/models/distributed_lightfm_wrap/lightfm_wrap.py
@@ -0,0 +1,757 @@
+import logging
+import multiprocessing as mp
+from datetime import timedelta
+from os.path import join
+from typing import Any, Dict, Optional, Tuple, List
+
+import numpy as np
+import pandas as pd
+from lightfm import LightFM
+from pyspark.sql import DataFrame, SparkSession
+from pyspark.sql import functions as sf
+from pyspark.sql.types import (
+    ArrayType,
+    FloatType,
+    DoubleType,
+    IntegerType,
+    StructField,
+    StructType,
+)
+import pygloo as pgl
+import scipy.sparse as sp
+from sklearn.preprocessing import MinMaxScaler
+import torch.distributed as dist
+
+from replay.models.base_rec import HybridRecommender
+from replay.models.distributed_lightfm_wrap.utils import LightFMTraining
+from replay.ann.ann_mixin import ANNMixin
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.session_handler import State
+from replay.utils import (
+    check_numeric,
+    load_pickled_from_parquet,
+    save_picklable_to_parquet,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-arguments, unnecessary-dunder-call
+class DistributedLightFMWrap(HybridRecommender, ANNMixin):
+    """Wrapper for distributed version of the LightFM."""
+
+    model_weights = (
+        "item_embeddings",
+        "item_embedding_gradients",
+        "item_embedding_momentum",
+        "item_biases",
+        "item_bias_gradients",
+        "item_bias_momentum",
+        "user_embeddings",
+        "user_embedding_gradients",
+        "user_embedding_momentum",
+        "user_biases",
+        "user_bias_gradients",
+        "user_bias_momentum",
+    )
+    losses = ["warp", "bpr", "warp-kos", "logistic"]
+
+    @staticmethod
+    def _get_entity_name(df: DataFrame):
+        return "item" if "item_idx" in df.columns else "user"
+
+    def _get_features(
+            self, ids: DataFrame, features: Optional[DataFrame],
+    ) -> Tuple[Optional[DataFrame], Optional[int]]:
+        if self.model is None:
+            raise AttributeError("Model has not been fitted yet.")
+        entity = self._get_entity_name(ids)
+        if features is not None:
+            features = self._convert_features_to_csr(
+                ids.select(f"{entity}_idx").distinct(), features
+            )
+        _biases, representations = self.model.__getattribute__(
+            f"get_{entity}_representations"
+        )(features=features)
+
+        def _representations(
+                representations_arr: np.ndarray, biases_arr: np.ndarray
+        ):
+            biases_arr = biases_arr.tolist()
+            for entity_idx in range(representations_arr.shape[0]):
+                yield entity_idx, representations_arr[
+                    entity_idx
+                ].tolist(), biases_arr[entity_idx]
+
+        lightfm_factors = State().session.createDataFrame(
+            _representations(representations, _biases),
+            schema=StructType(
+                [
+                    StructField(f"{entity}_idx", IntegerType()),
+                    StructField(f"{entity}_factors", ArrayType(DoubleType())),
+                    StructField(f"{entity}_bias", DoubleType()),
+                ]
+            ),
+        )
+        return (
+            lightfm_factors.join(ids, how="right", on=f"{entity}_idx"),
+            self.model.no_components,
+        )
+
+    def _get_vectors_to_build_ann(self, log: DataFrame) -> DataFrame:
+        item_vectors, _ = self._get_features(
+            log.select("item_idx").distinct(), None
+        )
+        return item_vectors
+
+    def _get_ann_build_params(self, log: DataFrame) -> Dict[str, Any]:
+        self.index_builder.index_params.dim = self.model.no_components
+        self.index_builder.index_params.max_elements = log.select("item_idx").distinct().count()
+        return {
+            "features_col": "item_factors",
+            "ids_col": "item_idx",
+        }
+
+    def _get_vectors_to_infer_ann_inner(
+            self, log: DataFrame, users: DataFrame
+    ) -> DataFrame:
+        user_vectors, _ = self._get_features(users, None)
+        return user_vectors
+
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        self.index_builder.index_params.dim = self.model.no_components
+        return {
+            "features_col": "user_factors",
+        }
+
+    @staticmethod
+    def unionify(df: DataFrame, df_2: Optional[DataFrame] = None) -> DataFrame:
+        if df_2 is not None:
+            df = df.unionByName(df_2)
+        return df
+
+    def _check_parameters(self):
+        assert self.loss in self.losses, f"Only `{'`, `'.join(self.losses)}` losses can be used."
+
+    def __init__(
+            self,
+            no_components: int = 10,
+            learning_schedule: str = "adagrad",
+            loss: str = "warp",
+            learning_rate: float = 0.05,
+            rho: float = 0.95,
+            epsilon: float = 1e-6,
+            k_kos_warp: int = 5,
+            n_kos_warp: int = 10,
+            max_sampled: int = 100,
+            random_state: Optional[int] = None,
+            tcp_port: int = 21235,
+            connection_timeout: int = 200,
+            index_builder: Optional[IndexBuilder] = None,
+            num_epochs: int = 30,
+            pygloo_timeout_sec: int = 200,
+    ):
+        """
+        :param no_components: the dimensionality of the feature latent embeddings
+        :param learning_schedule: learning schedule used on training. One of (‘adagrad’, ‘adadelta’)
+        :param loss: loss function. One of (‘logistic’, ‘bpr’, ‘warp’, ‘warp-kos’)
+        :param learning_rate: initial learning rate for the adagrad learning schedule
+        :param rho: moving average coefficient for the adadelta learning schedule
+        :param epsilon: conditioning parameter for the adadelta learning schedule
+        :param k_kos_warp: for k-OS training, the k-th positive example will be selected from the n positive examples
+            sampled for every user
+        :param n_kos_warp: for k-OS training, maximum number of positives sampled for each update
+        :param max_sampled: maximum number of negative samples used during WARP fitting
+        :param random_state: the random seed to use when shuffling the data and initializing the parameters
+        :param tcp_port: port of TCP store used in collective communications
+        :param connection_timeout: TCP store connection timeout in seconds
+        :param index_builder: IndexBuilder instance for ANN predictions
+        :param num_epochs: number of training epochs
+        :param pygloo_timeout_sec: collective operations timeout in seconds
+        """
+        if loss == "logistic":
+            self.logger.warning(
+                "Usage of distributed `logistic` loss can be unstable in performance."
+            )
+
+        self.no_components = no_components
+        self.learning_schedule = learning_schedule
+        self.loss = loss
+        self.learning_rate = learning_rate
+        self.rho = rho
+        self.epsilon = epsilon
+        self.k_kos_warp = k_kos_warp
+        self.n_kos_warp = n_kos_warp
+        self.max_sampled = max_sampled
+        self.random_state = random_state
+        self.tcp_port = tcp_port
+        self.connection_timeout = connection_timeout
+
+        if isinstance(index_builder, (IndexBuilder, type(None))):
+            self.index_builder = index_builder
+        elif isinstance(index_builder, dict):
+            self.init_builder_from_dict(index_builder)
+        self._pygloo_timeout_sec = pygloo_timeout_sec
+        self._num_epochs = num_epochs
+
+        self.model: Optional[LightFM] = None
+        self.world_size: Optional[int] = None
+        self.max_seen_user_idx: Optional[int] = None
+        self.max_seen_item_idx: Optional[int] = None
+        self.num_elements: Optional[int] = None
+        self.num_threads: Optional[int] = None
+        self.user_feat_scaler: Optional[MinMaxScaler] = None
+        self.item_feat_scaler: Optional[MinMaxScaler] = None
+
+        self._check_parameters()
+
+    @property
+    def num_epochs(self):
+        """ Get number of training epochs."""
+        return self._num_epochs
+
+    @num_epochs.setter
+    def num_epochs(self, value):
+        if value <= 0:
+            raise ValueError(
+                "Defined number of training epochs must be positive."
+            )
+        self._num_epochs = value
+
+    @property
+    def pygloo_timeout_sec(self):
+        """ Get timeout value in GLOO collective operations."""
+        return self._pygloo_timeout_sec
+
+    @pygloo_timeout_sec.setter
+    def pygloo_timeout_sec(self, value):
+        if value <= 0:
+            raise ValueError("Timout should be longer than 0 seconds.")
+        self._pygloo_timeout_sec = value
+
+    def _save_model(self, path: str):
+        for weight in self.model_weights:
+            value_to_save = self.model.__getattribute__(weight)
+            self.model.__setattr__(weight, None)
+            save_picklable_to_parquet(value_to_save, join(path, weight))
+
+        save_picklable_to_parquet(self.model, join(path, "LightFM_model"))
+        save_picklable_to_parquet(
+            self.user_feat_scaler, join(path, "user_feat_scaler")
+        )
+        save_picklable_to_parquet(
+            self.item_feat_scaler, join(path, "item_feat_scaler")
+        )
+
+        if self._use_ann:
+            self._save_index(path)
+
+    def _load_model(self, path: str):
+        self.model = load_pickled_from_parquet(join(path, "LightFM_model"))
+
+        for weight in self.model_weights:
+            self.model.__setattr__(
+                weight, load_pickled_from_parquet(join(path, weight))
+            )
+
+        self.user_feat_scaler = load_pickled_from_parquet(
+            join(path, "user_feat_scaler")
+        )
+        self.item_feat_scaler = load_pickled_from_parquet(
+            join(path, "item_feat_scaler")
+        )
+
+        if self._use_ann:
+            self._load_index(path)
+
+    @property
+    def _init_args(self) -> dict:
+        return {
+            "no_components": self.no_components,
+            "learning_schedule": self.learning_schedule,
+            "loss": self.loss,
+            "learning_rate": self.learning_rate,
+            "rho": self.rho,
+            "epsilon": self.epsilon,
+            "k_kos_warp": self.k_kos_warp,
+            "n_kos_warp": self.n_kos_warp,
+            "max_sampled": self.max_sampled,
+            "random_state": self.random_state,
+            "tcp_port": self.tcp_port,
+            "connection_timeout": self.connection_timeout,
+            "index_builder": self.index_builder.init_meta_as_dict() if self.index_builder else None,
+            "num_epochs": self._num_epochs,
+            "pygloo_timeout_sec": self._pygloo_timeout_sec,
+        }
+
+    def _initialize_world_size_and_threads(self):
+        # Get configurations parameters for Gloo workers
+        spark_sess = SparkSession.getActiveSession()
+        master_addr = spark_sess.conf.get("spark.master")
+        if master_addr.startswith("local-cluster"):
+            exec_str, cores_str, _ = master_addr[len("local-cluster["): -1].split(",")
+            num_executor_instances = int(exec_str)
+            num_executor_cores = int(cores_str)
+        elif master_addr.startswith("local"):
+            cores_str = master_addr[len("local["): -1]
+            num_executor_instances = 1
+            num_executor_cores = (int(cores_str) if cores_str != "*" else mp.cpu_count())
+        else:
+            num_executor_instances = int(spark_sess.conf.get("spark.executor.instances", "1"))
+            num_executor_cores = int(spark_sess.conf.get("spark.executor.cores", "1"))
+        self.world_size = max(num_executor_instances, 1)
+        self.num_threads = max(num_executor_cores, 1)
+
+    def _initialize_and_reset_model_state_and_scalers(self):
+        # Reset user / item scalers in case if model was initialized earlier
+        self.user_feat_scaler = None
+        self.item_feat_scaler = None
+        # Initialize / reset model states in case it was trained earlier
+        self.model = LightFM(
+            no_components=self.no_components,
+            learning_schedule=self.learning_schedule,
+            loss=self.loss,
+            learning_rate=self.learning_rate,
+            rho=self.rho,
+            epsilon=self.epsilon,
+            k=self.k_kos_warp,
+            n=self.n_kos_warp,
+            max_sampled=self.max_sampled,
+            random_state=self.random_state,
+        )
+
+    def _fit(
+            self,
+            log: DataFrame,
+            user_features: Optional[DataFrame] = None,
+            item_features: Optional[DataFrame] = None,
+    ) -> None:
+        self._initialize_and_reset_model_state_and_scalers()
+        self._fit_partial(log, user_features, item_features)
+
+    def _predict(
+            self,
+            log: DataFrame,
+            k: int,
+            users: DataFrame,
+            items: DataFrame,
+            user_features: Optional[DataFrame] = None,
+            item_features: Optional[DataFrame] = None,
+            filter_seen_items: bool = True,
+    ) -> DataFrame:
+
+        users = users.distinct()
+        items = items.distinct()
+        pairs = users.crossJoin(items)
+        predict = self._predict_selected_pairs(pairs, user_features, item_features)
+        return predict
+
+    def _predict_selected_pairs(
+            self,
+            pairs: DataFrame,
+            user_features: Optional[DataFrame] = None,
+            item_features: Optional[DataFrame] = None,
+    ) -> DataFrame:
+
+        @sf.pandas_udf(FloatType())
+        def predict_by_user(user_idx: pd.Series, item_idx: pd.Series) -> pd.Series:
+            return pd.Series(model.predict(
+                user_ids=user_idx.copy().to_numpy(),
+                item_ids=item_idx.copy().to_numpy(),
+                item_features=csr_item_features,
+                user_features=csr_user_features,
+            ))
+
+        model = self.model
+
+        if self.can_predict_cold_users and user_features is None:
+            raise ValueError("User features are missing for predict")
+        if self.can_predict_cold_items and item_features is None:
+            raise ValueError("Item features are missing for predict")
+
+        csr_item_features = self._convert_features_to_csr(
+            pairs.select("item_idx").distinct(), item_features
+        )
+        csr_user_features = self._convert_features_to_csr(
+            pairs.select("user_idx").distinct(), user_features
+        )
+
+        return pairs.withColumn('relevance', predict_by_user(sf.col("user_idx"), sf.col("item_idx")))
+
+    def _convert_features_to_csr(
+            self, entity_ids: DataFrame, features: Optional[DataFrame] = None
+    ) -> Optional[sp.csr_matrix]:
+        """
+        Convert DataFrame with features to scipy csr_matrix format if provided.
+
+        :param entity_ids: user/item_ids from log
+        :param features: user/item_features in DataFrame format
+        :return: np.float32 csr_matrix of shape [n_users, n_user_features], optional
+             Each row contains that user's weights over features.
+        """
+        if features is None:
+            return None
+
+        check_numeric(features)
+
+        entity = self._get_entity_name(features)
+        num_seen_entities = self.__getattribute__(f"max_seen_{entity}_idx")
+        # Filter out features and entities not presented in log or features DataFrame
+        features = features.join(entity_ids, on=f"{entity}_idx", how="inner")
+
+        max_passed_id = (entity_ids.agg({f"{entity}_idx": "max"}).first()[0] + 1)
+        # Get number of new entities, for which model has not been trained on
+        num_cold_entities = max(0, max_passed_id - self.__getattribute__(f"max_seen_{entity}_idx"))
+        sparse_features = sp.vstack(
+            [
+                # Identity features matrix
+                sp.eye(
+                    num_seen_entities, num_seen_entities + num_cold_entities
+                ),
+                # Empty features matrix
+                sp.csr_matrix(
+                    np.zeros(
+                        (
+                            num_cold_entities,
+                            num_seen_entities + num_cold_entities,
+                        )
+                    )
+                ),
+            ]
+        )
+
+        # features to (num_seen_entities + num_cold_entities) x num_features
+        feature_names = [
+            col_name
+            for col_name in features.columns
+            if col_name != f"{entity}_idx"
+        ]
+        features_np = (
+            features.select(f"{entity}_idx", *feature_names)
+            .toPandas()
+            .to_numpy()
+        )
+        feature_entity_ids = features_np[:, 0]
+        features_columns = features_np[:, 1:]
+        number_of_features = features_columns.shape[1]
+        # Scale down dense features
+        scaler_name = f"{entity}_feat_scaler"
+        # Check if scaler for entity was already initialized
+        if self.__getattribute__(scaler_name) is None:
+            if not features_columns.size:
+                raise ValueError(f"features for {entity}s from log are absent")
+            # Update entity scaler with fitted scaler
+            self.__setattr__(scaler_name, MinMaxScaler().fit(features_columns))
+
+        if features_columns.size:
+            features_dense = self.__getattribute__(scaler_name).transform(
+                features_columns
+            )
+
+            features = sp.csr_matrix(
+                (
+                    features_dense.ravel(),
+                    (
+                        np.repeat(feature_entity_ids, number_of_features),
+                        np.tile(
+                            np.arange(number_of_features),
+                            feature_entity_ids.shape[0],
+                        ),
+                    ),
+                ),
+                shape=(
+                    num_seen_entities + num_cold_entities,
+                    number_of_features,
+                ),
+            )
+        else:
+            features = sp.csr_matrix(
+                (num_seen_entities + num_cold_entities, number_of_features)
+            )
+        return sp.hstack([sparse_features, features])
+
+    def _get_num_users_and_items(
+            self, log: DataFrame, previous_log: Optional[DataFrame] = None
+    ) -> Tuple[int, int]:
+        if previous_log is not None:
+            previous_log = previous_log.select(log.columns)
+        interactions = self.unionify(log, previous_log)
+        num_users = interactions.agg({"user_idx": "max"}).first()[0]
+        num_items = interactions.agg({"item_idx": "max"}).first()[0]
+        return num_users + 1, num_items + 1
+
+    # pylint: disable=unused-argument
+    def _reinitialize_embeddings(
+            self,
+            log: DataFrame,
+            previous_log: DataFrame,
+            num_users: int,
+            num_items: int,
+            user_features: Optional[DataFrame] = None,
+            item_features: Optional[DataFrame] = None,
+    ) -> None:
+        """
+        Partial fit model states reinitialization to adjust previous and current training logs.
+
+        :param log: training log
+        :param previous_log: previous training log
+        :param num_users: number of users in log
+        :param num_items: number of items in log
+        :param user_features: user features in csr_matrix format (num_users x num_user_features)
+        :param item_features: item features in csr_matrix format (num_items x num_item_features)
+        :return:
+        """
+        # Get number of users and items presented in previous log
+        (
+            num_users_previous_log,
+            num_items_previous_log,
+        ) = self._get_num_users_and_items(previous_log)
+
+        # Get number of users and items added in log
+        new_users = num_users - num_users_previous_log
+        new_items = num_items - num_items_previous_log
+
+        # Get number of non-I users` and items` features in previous log and log
+        num_user_extra_features_previous_log = (
+                self.model.user_embeddings.shape[0] - num_users_previous_log
+        )
+        num_item_extra_features_previous_log = (
+                self.model.item_embeddings.shape[0] - num_items_previous_log
+        )
+
+        if (
+            self.model.user_embeddings.shape[0] - num_user_extra_features_previous_log != num_users_previous_log
+        ) or (
+            self.model.item_embeddings.shape[0] - num_item_extra_features_previous_log != num_items_previous_log
+        ):
+            raise ValueError(
+                "Number of user/items features in trained model is not equal "
+                "to number of users/items in previous log."
+            )
+        # Get number of non-I features if entities features are passed
+        num_user_extra_features_log = user_features.shape[1] - num_users if user_features is not None else 0
+        num_item_extra_features_log = item_features.shape[1] - num_items if item_features is not None else 0
+
+        if not (
+                num_user_extra_features_previous_log == num_user_extra_features_log
+                and num_item_extra_features_previous_log
+                == num_item_extra_features_log
+        ):
+            raise ValueError(
+                f"Passed number of extra features for user: {num_user_extra_features_log} \n"
+                f"Passed number of extra features for item: {num_item_extra_features_log}. \n"
+                f"But model was trained on {num_user_extra_features_previous_log} user and "
+                f"{num_item_extra_features_previous_log} item features."
+            )
+
+        # Initialise new item features.
+        new_item_embeddings = (
+                (self.model.random_state.rand(new_items, self.no_components) - 0.5)
+                / self.no_components
+        ).astype(np.float32)
+        new_item_embedding_gradients = np.zeros_like(new_item_embeddings)
+        new_item_embedding_momentum = np.zeros_like(new_item_embeddings)
+        new_item_biases = np.zeros(new_items, dtype=np.float32)
+        new_item_bias_gradients = np.zeros_like(new_item_biases)
+        new_item_bias_momentum = np.zeros_like(new_item_biases)
+
+        # Initialise new user features.
+        new_user_embeddings = (
+                (self.model.random_state.rand(new_users, self.no_components) - 0.5)
+                / self.no_components
+        ).astype(np.float32)
+        new_user_embedding_gradients = np.zeros_like(new_user_embeddings)
+        new_user_embedding_momentum = np.zeros_like(new_user_embeddings)
+        new_user_biases = np.zeros(new_users, dtype=np.float32)
+        new_user_bias_gradients = np.zeros_like(new_user_biases)
+        new_user_bias_momentum = np.zeros_like(new_user_biases)
+
+        def _concat_arrays(model_array: np.ndarray, added_array: np.ndarray, dissect_at: int):
+            return np.concatenate(
+                (
+                    model_array[:dissect_at],
+                    added_array,
+                    model_array[dissect_at:],
+                )
+            )
+
+        def _update_values(entity_updates: List[Tuple[str, np.ndarray]], num_entities_previous_log: int) -> None:
+            for attribute_name, value in entity_updates:
+                # Concatenate new and old features preserving the identity and non identity features order
+                updated_value = _concat_arrays(
+                    self.model.__getattribute__(attribute_name),
+                    value,
+                    num_entities_previous_log,
+                )
+                self.model.__setattr__(attribute_name, updated_value)
+
+        item_updates = [
+            ("item_embeddings", new_item_embeddings),
+            ("item_embedding_gradients", new_item_embedding_gradients),
+            ("item_embedding_momentum", new_item_embedding_momentum),
+            ("item_biases", new_item_biases),
+            ("item_bias_gradients", new_item_bias_gradients),
+            ("item_bias_momentum", new_item_bias_momentum),
+        ]
+        user_updates = [
+            ("user_embeddings", new_user_embeddings),
+            ("user_embedding_gradients", new_user_embedding_gradients),
+            ("user_embedding_momentum", new_user_embedding_momentum),
+            ("user_biases", new_user_biases),
+            ("user_bias_gradients", new_user_bias_gradients),
+            ("user_bias_momentum", new_user_bias_momentum),
+        ]
+
+        _update_values(item_updates, num_items_previous_log)
+        _update_values(user_updates, num_users_previous_log)
+
+    # pylint: disable=unused-variable, too-many-statements
+    def _fit_partial(
+            self,
+            log: DataFrame,
+            user_features: Optional[DataFrame] = None,
+            item_features: Optional[DataFrame] = None,
+            previous_log: Optional[DataFrame] = None,
+    ) -> None:
+        if self.model is None:
+            # If fit partial called on non initialized model
+            self._initialize_and_reset_model_state_and_scalers()
+        self._initialize_world_size_and_threads()
+
+        self.can_predict_cold_users = user_features is not None
+        self.can_predict_cold_items = item_features is not None
+
+        # Prepare interactions log
+        log = log.select("user_idx", "item_idx", sf.lit(1).alias("relevance"))
+        log = log.repartition(self.world_size, "user_idx")
+
+        n_users, n_items = self._get_num_users_and_items(
+            log=log, previous_log=previous_log
+        )
+        # Update value of maximum seen users and items number
+        self.max_seen_user_idx = n_users
+        self.max_seen_item_idx = n_items
+        user_features = self._convert_features_to_csr(log.select("user_idx").distinct(), user_features)
+        item_features = self._convert_features_to_csr(log.select("item_idx").distinct(), item_features)
+        # Update model`s embeddings and biases if model was initialized and trained on previous log
+        if previous_log is not None:
+            if self.model.item_embeddings is not None:
+                self._reinitialize_embeddings(
+                    log,
+                    previous_log,
+                    num_users=n_users,
+                    num_items=n_items,
+                    user_features=user_features,
+                    item_features=item_features,
+                )
+            else:
+                raise ValueError(
+                    "Passed previous log will not affect training "
+                    "as the model has not been trained yet."
+                )
+
+        (
+            user_features,
+            item_features,
+        ) = self.model._construct_feature_matrices(n_users, n_items, user_features, item_features)
+
+        for input_data in (
+                user_features.data,
+                item_features.data,
+        ):
+            self.model._check_input_finite(input_data)
+
+        if self.model.item_embeddings is None:
+            self.model._initialize(
+                self.model.no_components,
+                item_features.shape[1],
+                user_features.shape[1],
+            )
+
+        if not item_features.shape[1] == self.model.item_embeddings.shape[0]:
+            raise ValueError("Incorrect number of features in item_features")
+        if not user_features.shape[1] == self.model.user_embeddings.shape[0]:
+            raise ValueError("Incorrect number of features in user_features")
+        if self.num_threads < 1:
+            raise ValueError("Number of threads must be 1 or larger.")
+        # Initialize trainer instance to perform distributed training using Gloo workers
+        training_instance = LightFMTraining(self.model, self.world_size, self.num_threads)
+        # Initialize TCP store for collective communications
+        host = training_instance.get_host_ip()
+        real_store = dist.TCPStore(
+            host,
+            self.tcp_port,
+            self.world_size + 1,
+            True,
+            timedelta(seconds=self.connection_timeout),
+        )
+
+        _tcp_port = self.tcp_port
+        _connection_timeout = self.connection_timeout
+        _num_epochs = self._num_epochs
+        _pygloo_timeout = self._pygloo_timeout_sec
+
+        def udf_to_map_on_interactions_with_index(
+                p_idx, partition_interactions
+        ):
+            # Initialize pygloo context
+            context = pgl.rendezvous.Context(p_idx, training_instance.world_size)
+            local_ip = training_instance.get_host_ip()
+            attr = pgl.transport.tcp.attr(local_ip)
+            dev = pgl.transport.tcp.CreateDevice(attr)
+            local_store = dist.TCPStore(
+                host,
+                _tcp_port,
+                training_instance.world_size + 1,
+                False,
+                timedelta(seconds=_connection_timeout),
+            )
+            store = pgl.rendezvous.CustomStore(local_store)
+            context.setTimeout(timedelta(seconds=_pygloo_timeout))
+            context.connectFullMesh(store, dev)
+            gloo_context = context
+
+            # Prapare data
+            interactions = training_instance.rdd_to_csr(partition_interactions, num_users=n_users, num_items=n_items)
+            interactions = interactions.tocoo()
+
+            if interactions.dtype != np.float32:
+                interactions.data = interactions.data.astype(np.float32)
+
+            # Initialize sample weights
+            sample_weight_data = training_instance.model._process_sample_weight(
+                interactions, sample_weight=None
+            )
+            for input_data in (
+                    interactions.data,
+                    sample_weight_data,
+            ):
+                training_instance.model._check_input_finite(input_data)
+
+            # Get local interactions partition in COO sparse matrix format
+            interactions_part = sp.coo_matrix(
+                (interactions.data, (interactions.row, interactions.col)),
+                shape=(n_users, n_items),
+            )
+
+            # Copy model states to executors
+            local_training_features = (
+                training_instance.initialize_local_state()
+            )
+
+            # Each Spark executor runs on interaction matrix partition
+            for _epoch_num in training_instance.model._progress(_num_epochs, verbose=False):
+                training_instance.run_epoch_spark(
+                    item_features,
+                    user_features,
+                    interactions_part,
+                    sample_weight_data,
+                    gloo_context,
+                    local_training_features,
+                )
+                training_instance.model._check_finite()
+
+            if p_idx == 0:
+                yield training_instance.model
+
+        self.model = log.rdd.mapPartitionsWithIndex(udf_to_map_on_interactions_with_index).first()
diff --git a/replay/models/distributed_lightfm_wrap/utils.py b/replay/models/distributed_lightfm_wrap/utils.py
new file mode 100644
index 000000000..8361106e7
--- /dev/null
+++ b/replay/models/distributed_lightfm_wrap/utils.py
@@ -0,0 +1,369 @@
+import itertools
+import socket
+from dataclasses import dataclass
+from typing import Tuple, Union
+
+import numpy as np
+from lightfm import LightFM
+from lightfm._lightfm_fast import (
+    CSRMatrix,
+    FastLightFM,
+    fit_bpr,
+    fit_logistic,
+    fit_warp,
+    fit_warp_kos,
+)
+import pygloo as pgl
+
+from scipy.sparse import csr_matrix
+
+
+@dataclass
+class ModelFeatures:
+    """ Model representations. """
+
+    item_features: np.ndarray
+    item_biases: np.ndarray
+    user_features: np.ndarray
+    user_biases: np.ndarray
+
+
+# pylint: disable=too-many-instance-attributes, unnecessary-dunder-call
+@dataclass
+class ModelTrainingFeatures:
+    """ Model features used on training. """
+
+    item_feature_gradients: np.ndarray
+    item_feature_momentum: np.ndarray
+    item_bias_gradients: np.ndarray
+    item_bias_momentum: np.ndarray
+    user_feature_gradients: np.ndarray
+    user_feature_momentum: np.ndarray
+    user_bias_gradients: np.ndarray
+    user_bias_momentum: np.ndarray
+
+    user_alpha: np.ndarray
+    item_alpha: np.ndarray
+
+
+# pylint: disable=too-many-instance-attributes, too-many-arguments
+class LightFMTraining:
+    """LightFMTraining class used in distributed LightFM to perform training steps and results synchronization.
+
+    To perform a LightFM model training in a distributed fashion, each Spark executor is initialized as a Gloo worker.
+     The interation matrix is divided between executors. Additionally, OpenMP threads are initialized according to
+     the number of Spark executor cores.
+     Before the training begins, interaction matrix is partitioned by `user_idx`, model states (item and user embeddings
+     and biases, along with training features) are copied to the Spark executors. Then, the Gloo context is initialized,
+     each executor is registered as the Gloo worker, each of which will train the model on the part of the interaction
+     matrix.
+     During the training, on each epoch, model`s weights and user/item representations updates are performed separately
+     by Gloo workers. At the end of each epoch, worker collects the updates and AllReduce collective operation is
+     performed to synchronize the results with other Gloo workers.
+
+    :param model: LightFM model instance
+    :param world_size: collective communication world size - number of Gloo workers
+    :param num_threads: number of threads for OpenMP parallelization within an executor
+    """
+
+    def __init__(self, model: LightFM, world_size: int, num_threads: int):
+        self.model = model
+        self.world_size = world_size
+        self.num_threads = num_threads
+
+    def initialize_local_state(self) -> ModelTrainingFeatures:
+        """ Create local copy of the model states. """
+        return ModelTrainingFeatures(
+            item_feature_gradients=self.model.item_embedding_gradients.copy(),
+            item_feature_momentum=self.model.item_embedding_momentum.copy(),
+            item_bias_gradients=self.model.item_bias_gradients.copy(),
+            item_bias_momentum=self.model.item_bias_momentum.copy(),
+            user_feature_gradients=self.model.user_embedding_gradients.copy(),
+            user_feature_momentum=self.model.user_embedding_momentum.copy(),
+            user_bias_gradients=self.model.user_bias_gradients.copy(),
+            user_bias_momentum=self.model.user_bias_momentum.copy(),
+            user_alpha=self.model.user_alpha,
+            item_alpha=self.model.item_alpha,
+        )
+
+    @staticmethod
+    def rdd_to_csr(
+        partition_interactions: itertools.chain, num_users: int, num_items: int
+    ):
+        """ Convert Spark RDD into scipy CSR matrix."""
+        user_ids, item_ids, relevance = [], [], []
+        for row in partition_interactions:
+            user_ids.append(row.user_idx)
+            item_ids.append(row.item_idx)
+            relevance.append(row.relevance)
+
+        csr = csr_matrix(
+            (relevance, (user_ids, item_ids)), shape=(num_users, num_items),
+        )
+        return csr
+
+    @staticmethod
+    def get_host_ip():
+        """ Get the IP of the host. """
+        hostname = socket.gethostname()
+        ip_address = socket.gethostbyname(hostname)
+        return ip_address
+
+    def _copy_representations_for_update(self) -> ModelFeatures:
+        """ Create local copy of the item and user representations. """
+        return ModelFeatures(
+            item_features=self.model.item_embeddings.copy(),
+            item_biases=self.model.item_biases.copy(),
+            user_features=self.model.user_embeddings.copy(),
+            user_biases=self.model.user_biases.copy(),
+        )
+
+    def _get_lightfm_data(
+        self, features: ModelFeatures, training_features: ModelTrainingFeatures
+    ) -> FastLightFM:
+        """ Create FastLightFM class from the states to run update. """
+
+        lightfm_data = FastLightFM(
+            features.item_features,
+            training_features.item_feature_gradients,
+            training_features.item_feature_momentum,
+            features.item_biases,
+            training_features.item_bias_gradients,
+            training_features.item_bias_momentum,
+            features.user_features,
+            training_features.user_feature_gradients,
+            training_features.user_feature_momentum,
+            features.user_biases,
+            training_features.user_bias_gradients,
+            training_features.user_bias_momentum,
+            self.model.no_components,
+            int(self.model.learning_schedule == "adadelta"),
+            self.model.learning_rate,
+            self.model.rho,
+            self.model.epsilon,
+            self.model.max_sampled,
+        )
+
+        return lightfm_data
+
+    def run_epoch_spark(
+        self,
+        item_features,
+        user_features,
+        interactions,
+        sample_weight,
+        gloo_context,
+        training_features: ModelTrainingFeatures,
+    ):
+        """ Run an epoch of training. """
+        if self.model.loss in ("warp", "bpr", "warp-kos",):
+            positives_lookup = CSRMatrix(
+                self.model._get_positives_lookup_matrix(interactions)
+            )
+
+        shuffle_indices = np.arange(len(interactions.data), dtype=np.int32)
+        self.model.random_state.shuffle(shuffle_indices)
+
+        # Get representations copies from the local model
+        local_features = self._copy_representations_for_update()
+        lightfm_data = self._get_lightfm_data(
+            features=local_features, training_features=training_features
+        )
+
+        if self.model.loss == "warp":
+            # Run updates on the model state copy
+            fit_warp(
+                CSRMatrix(item_features),
+                CSRMatrix(user_features),
+                positives_lookup,
+                interactions.row,
+                interactions.col,
+                interactions.data,
+                sample_weight,
+                shuffle_indices,
+                lightfm_data,
+                self.model.learning_rate,
+                training_features.item_alpha,
+                training_features.user_alpha,
+                self.num_threads,
+                self.model.random_state,
+            )
+        elif self.model.loss == "bpr":
+            fit_bpr(
+                CSRMatrix(item_features),
+                CSRMatrix(user_features),
+                positives_lookup,
+                interactions.row,
+                interactions.col,
+                interactions.data,
+                sample_weight,
+                shuffle_indices,
+                lightfm_data,
+                self.model.learning_rate,
+                training_features.item_alpha,
+                training_features.user_alpha,
+                self.num_threads,
+                self.model.random_state,
+            )
+        elif self.model.loss == "warp-kos":
+            fit_warp_kos(
+                CSRMatrix(item_features),
+                CSRMatrix(user_features),
+                positives_lookup,
+                interactions.row,
+                shuffle_indices,
+                lightfm_data,
+                self.model.learning_rate,
+                training_features.item_alpha,
+                training_features.user_alpha,
+                self.model.k,
+                self.model.n,
+                self.num_threads,
+                self.model.random_state,
+            )
+        elif self.model.loss == "logistic":
+            fit_logistic(
+                CSRMatrix(item_features),
+                CSRMatrix(user_features),
+                interactions.row,
+                interactions.col,
+                interactions.data,
+                sample_weight,
+                shuffle_indices,
+                lightfm_data,
+                self.model.learning_rate,
+                training_features.item_alpha,
+                training_features.user_alpha,
+                self.num_threads,
+            )
+        else:
+            raise NotImplementedError(
+                "Only `warp`, `bpr` losses are available by the moment"
+            )
+
+        # Get embeddings deltas before reduction
+        local_features = self._get_update_delta_after_fit(local_features)
+        # Perform AllReduce reduction on local states
+        local_features, training_features = self._reduce_states_on_workers(
+            gloo_context, local_features, training_features
+        )
+        # Update local models with common model states
+        self._update_model_with_reduced_data(local_features, training_features)
+
+    def _get_update_delta_after_fit(
+        self, local_features: ModelFeatures
+    ) -> ModelFeatures:
+        """ Extract initial representation values to get delta from update. """
+        local_features.item_features -= self.model.item_embeddings
+        local_features.item_biases -= self.model.item_biases
+        local_features.user_features -= self.model.user_embeddings
+        local_features.user_biases -= self.model.user_biases
+        return local_features
+
+    def _reduce_states_on_workers(
+        self,
+        gloo_context,
+        features: ModelFeatures,
+        training_features: ModelTrainingFeatures,
+    ) -> Tuple[ModelFeatures, ModelTrainingFeatures]:
+        """ Perform AllReduce operation on representations and optimization parameters. """
+
+        def reduce_(
+            item: Union[ModelTrainingFeatures, ModelFeatures],
+            attr_name: str,
+            operation: str = "sum",
+        ) -> Union[ModelTrainingFeatures, ModelFeatures]:
+            sendbuf = item.__getattribute__(attr_name)
+            recvbuf = np.zeros_like(sendbuf, dtype=np.float32)
+            sendptr = sendbuf.ctypes.data
+            recvptr = recvbuf.ctypes.data
+            data_size = (
+                sendbuf.size
+                if isinstance(sendbuf, np.ndarray)
+                else sendbuf.numpy().size
+            )
+            datatype = pgl.glooDataType_t.glooFloat32
+
+            pgl.allreduce(
+                gloo_context,
+                sendptr,
+                recvptr,
+                data_size,
+                datatype,
+                pgl.ReduceOp.SUM,
+                pgl.allreduceAlgorithm.RING,
+            )
+
+            item.__setattr__(attr_name, recvbuf)
+            if operation == "avg":
+                attr_value = item.__getattribute__(attr_name)
+                item.__setattr__(attr_name, attr_value / self.world_size)
+
+            return item
+
+        avg_attributes_features = (
+            "item_features",
+            "item_biases",
+        )
+        sum_attributes_features = (
+            "user_features",
+            "user_biases",
+        )
+        avg_attributes_training_features = (
+            "item_feature_gradients",
+            "item_feature_momentum",
+            "item_bias_gradients",
+            "item_bias_momentum",
+            "user_feature_gradients",
+            "user_feature_momentum",
+            "user_bias_gradients",
+            "user_bias_momentum",
+        )
+
+        for sum_attributes_feat in sum_attributes_features:
+            features = reduce_(
+                features, attr_name=sum_attributes_feat, operation="sum"
+            )
+
+        for avg_attributes_feat in avg_attributes_features:
+            features = reduce_(
+                features, attr_name=avg_attributes_feat, operation="avg"
+            )
+
+        for avg_attributes_tr_feat in avg_attributes_training_features:
+            training_features = reduce_(
+                training_features,
+                attr_name=avg_attributes_tr_feat,
+                operation="avg",
+            )
+
+        return features, training_features
+
+    def _update_model_with_reduced_data(
+        self,
+        features: ModelFeatures,
+        training_features: ModelTrainingFeatures,
+    ):
+        """ Updates model state after parallel operations. """
+
+        self.model.item_embeddings += features.item_features
+        self.model.item_embedding_gradients = (
+            training_features.item_feature_gradients
+        )
+        self.model.item_embedding_momentum = (
+            training_features.item_feature_momentum
+        )
+        self.model.item_biases += features.item_biases
+        self.model.item_bias_gradients = training_features.item_bias_gradients
+        self.model.item_bias_momentum = training_features.item_bias_momentum
+
+        self.model.user_embeddings += features.user_features
+        self.model.user_embedding_gradients = (
+            training_features.user_feature_gradients
+        )
+        self.model.user_embedding_momentum = (
+            training_features.user_feature_momentum
+        )
+        self.model.user_biases += features.user_biases
+        self.model.user_bias_gradients = training_features.user_bias_gradients
+        self.model.user_bias_momentum = training_features.user_bias_momentum
diff --git a/replay/models/knn.py b/replay/models/knn.py
index da6675af8..2135cfffb 100644
--- a/replay/models/knn.py
+++ b/replay/models/knn.py
@@ -1,16 +1,23 @@
-from typing import Optional
+from typing import Optional, Dict, Any
 
 from pyspark.sql import DataFrame
 from pyspark.sql import functions as sf
 from pyspark.sql.window import Window
 
-from replay.models.base_rec import NeighbourRec
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.models.base_neighbour_rec import NeighbourRec
 from replay.optuna_objective import ItemKNNObjective
 
 
+# pylint: disable=too-many-ancestors
 class ItemKNN(NeighbourRec):
     """Item-based ItemKNN with modified cosine similarity measure."""
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        return {
+            "features_col": None,
+        }
+
     all_items: Optional[DataFrame]
     dot_products: Optional[DataFrame]
     item_norms: Optional[DataFrame]
@@ -23,18 +30,21 @@ class ItemKNN(NeighbourRec):
         "weighting": {"type": "categorical", "args": [None, "tf_idf", "bm25"]}
     }
 
-    def __init__(
+    def __init__(  # pylint: disable=too-many-arguments
         self,
         num_neighbours: int = 10,
         use_relevance: bool = False,
         shrink: float = 0.0,
         weighting: str = None,
+        index_builder: Optional[IndexBuilder] = None,
     ):
         """
         :param num_neighbours: number of neighbours
         :param use_relevance: flag to use relevance values as is or to treat them as 1
         :param shrink: term added to the denominator when calculating similarity
         :param weighting: item reweighting type, one of [None, 'tf_idf', 'bm25']
+        :param index_builder: `IndexBuilder` instance that adds ANN functionality.
+            If not set, then ann will not be used.
         """
         self.shrink = shrink
         self.use_relevance = use_relevance
@@ -44,6 +54,10 @@ def __init__(
         if weighting not in valid_weightings:
             raise ValueError(f"weighting must be one of {valid_weightings}")
         self.weighting = weighting
+        if isinstance(index_builder, (IndexBuilder, type(None))):
+            self.index_builder = index_builder
+        elif isinstance(index_builder, dict):
+            self.init_builder_from_dict(index_builder)
 
     @property
     def _init_args(self):
@@ -52,8 +66,17 @@ def _init_args(self):
             "use_relevance": self.use_relevance,
             "num_neighbours": self.num_neighbours,
             "weighting": self.weighting,
+            "index_builder": self.index_builder.init_meta_as_dict() if self.index_builder else None,
         }
 
+    def _save_model(self, path: str):
+        if self._use_ann:
+            self._save_index(path)
+
+    def _load_model(self, path: str):
+        if self._use_ann:
+            self._load_index(path)
+
     @staticmethod
     def _shrink(dot_products: DataFrame, shrink: float) -> DataFrame:
         return dot_products.withColumn(
diff --git a/replay/models/slim.py b/replay/models/slim.py
index e304ea02e..5cce6d1dd 100644
--- a/replay/models/slim.py
+++ b/replay/models/slim.py
@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Dict, Any
 
 import numpy as np
 import pandas as pd
@@ -7,14 +7,22 @@
 from scipy.sparse import csc_matrix
 from sklearn.linear_model import ElasticNet
 
-from replay.models.base_rec import NeighbourRec
+from replay.ann.index_builders.base_index_builder import IndexBuilder
+from replay.models.base_neighbour_rec import NeighbourRec
 from replay.session_handler import State
 
 
+# pylint: disable=too-many-ancestors
 class SLIM(NeighbourRec):
     """`SLIM: Sparse Linear Methods for Top-N Recommender Systems
     <http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf>`_"""
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        return {
+            "features_col": None,
+            # "params": self._nmslib_hnsw_params,
+        }
+
     _search_space = {
         "beta": {"type": "loguniform", "args": [1e-6, 5]},
         "lambda_": {"type": "loguniform", "args": [1e-6, 2]},
@@ -25,21 +33,41 @@ def __init__(
         beta: float = 0.01,
         lambda_: float = 0.01,
         seed: Optional[int] = None,
+        index_builder: Optional[IndexBuilder] = None,
     ):
         """
         :param beta: l2 regularization
         :param lambda_: l1 regularization
         :param seed: random seed
+        :param index_builder: `IndexBuilder` instance that adds ANN functionality.
+            If not set, then ann will not be used.
         """
         if beta < 0 or lambda_ <= 0:
             raise ValueError("Invalid regularization parameters")
         self.beta = beta
         self.lambda_ = lambda_
         self.seed = seed
+        if isinstance(index_builder, (IndexBuilder, type(None))):
+            self.index_builder = index_builder
+        elif isinstance(index_builder, dict):
+            self.init_builder_from_dict(index_builder)
 
     @property
     def _init_args(self):
-        return {"beta": self.beta, "lambda_": self.lambda_, "seed": self.seed}
+        return {
+            "beta": self.beta,
+            "lambda_": self.lambda_,
+            "seed": self.seed,
+            "index_builder": self.index_builder.init_meta_as_dict() if self.index_builder else None,
+        }
+
+    def _save_model(self, path: str):
+        if self._use_ann:
+            self._save_index(path)
+
+    def _load_model(self, path: str):
+        if self._use_ann:
+            self._load_index(path)
 
     def _fit(
         self,
diff --git a/replay/models/word2vec.py b/replay/models/word2vec.py
index 62c00fcf8..3113ea6c4 100644
--- a/replay/models/word2vec.py
+++ b/replay/models/word2vec.py
@@ -1,21 +1,58 @@
-from typing import Optional
+from typing import Optional, Dict, Any
 
 from pyspark.ml.feature import Word2Vec
+from pyspark.ml.functions import vector_to_array
+from pyspark.ml.stat import Summarizer
 from pyspark.sql import DataFrame
 from pyspark.sql import functions as sf
 from pyspark.sql import types as st
-from pyspark.ml.stat import Summarizer
 
+from replay.ann.ann_mixin import ANNMixin
+from replay.ann.index_builders.base_index_builder import IndexBuilder
 from replay.models.base_rec import Recommender, ItemVectorModel
 from replay.utils import vector_dot, multiply_scala_udf, join_with_col_renaming
 
 
-# pylint: disable=too-many-instance-attributes
-class Word2VecRec(Recommender, ItemVectorModel):
+# pylint: disable=too-many-instance-attributes, too-many-ancestors
+class Word2VecRec(Recommender, ItemVectorModel, ANNMixin):
     """
     Trains word2vec model where items ar treated as words and users as sentences.
     """
 
+    def _get_ann_infer_params(self) -> Dict[str, Any]:
+        self.index_builder.index_params.dim = self.rank
+        return {
+            "features_col": "user_vector",
+        }
+
+    def _get_vectors_to_infer_ann_inner(self, log: DataFrame, users: DataFrame) -> DataFrame:
+        user_vectors = self._get_user_vectors(users, log)
+        # converts to pandas_udf compatible format
+        user_vectors = user_vectors.select(
+            "user_idx", vector_to_array("user_vector").alias("user_vector")
+        )
+        return user_vectors
+
+    def _get_ann_build_params(self, log: DataFrame) -> Dict[str, Any]:
+        self.index_builder.index_params.dim = self.rank
+        self.index_builder.index_params.max_elements = log.select("item_idx").distinct().count()
+        self.logger.debug("index 'num_elements' = %s", self.num_elements)
+        return {
+            "features_col": "item_vector",
+            "ids_col": "item_idx"
+        }
+
+    def _get_vectors_to_build_ann(self, log: DataFrame) -> DataFrame:
+        item_vectors = self._get_item_vectors()
+        item_vectors = (
+            item_vectors
+            .select(
+                "item_idx",
+                vector_to_array("item_vector").alias("item_vector")
+            )
+        )
+        return item_vectors
+
     idf: DataFrame
     vectors: DataFrame
 
@@ -37,6 +74,7 @@ def __init__(
         use_idf: bool = False,
         seed: Optional[int] = None,
         num_partitions: Optional[int] = None,
+        index_builder: Optional[IndexBuilder] = None,
     ):
         """
         :param rank: embedding size
@@ -47,6 +85,8 @@ def __init__(
         :param window_size: window size
         :param use_idf: flag to use inverse document frequency
         :param seed: random seed
+        :param index_builder: `IndexBuilder` instance that adds ANN functionality.
+            If not set, then ann will not be used.
         """
 
         self.rank = rank
@@ -57,6 +97,11 @@ def __init__(
         self.max_iter = max_iter
         self._seed = seed
         self._num_partitions = num_partitions
+        if isinstance(index_builder, (IndexBuilder, type(None))):
+            self.index_builder = index_builder
+        elif isinstance(index_builder, dict):
+            self.init_builder_from_dict(index_builder)
+        self.num_elements = None
 
     @property
     def _init_args(self):
@@ -68,8 +113,26 @@ def _init_args(self):
             "step_size": self.step_size,
             "max_iter": self.max_iter,
             "seed": self._seed,
+            "index_builder": self.index_builder.init_meta_as_dict() if self.index_builder else None,
         }
 
+    def _save_model(self, path: str):
+        # # create directory on shared disk or in HDFS
+        # path_info = get_filesystem(path)
+        # destination_filesystem, target_dir_path = fs.FileSystem.from_uri(
+        #     path_info.hdfs_uri + path_info.path
+        #     if path_info.filesystem == FileSystem.HDFS
+        #     else path_info.path
+        # )
+        # destination_filesystem.create_dir(target_dir_path)
+
+        if self.index_builder:
+            self._save_index(path)
+
+    def _load_model(self, path: str):
+        if self.index_builder:
+            self._load_index(path)
+
     def _fit(
         self,
         log: DataFrame,
diff --git a/replay/scenarios/two_stages/two_stages_scenario.py b/replay/scenarios/two_stages/two_stages_scenario.py
index 26816845d..d7f188029 100644
--- a/replay/scenarios/two_stages/two_stages_scenario.py
+++ b/replay/scenarios/two_stages/two_stages_scenario.py
@@ -90,7 +90,7 @@ def get_first_level_model_features(
         )
         factors_to_explode.append(("item_factors", "if"))
 
-    if model.__str__() == "LightFMWrap":
+    if model.__str__() in ["LightFMWrap", "DistributedLightFMWrap"]:
         pairs_with_features = (
             pairs_with_features.fillna({"user_bias": 0, "item_bias": 0})
             .withColumnRenamed("user_bias", f"{prefix}_user_bias")
diff --git a/replay/session_handler.py b/replay/session_handler.py
index 8aa012bdd..f156f64fd 100644
--- a/replay/session_handler.py
+++ b/replay/session_handler.py
@@ -50,6 +50,7 @@ def get_spark_session(
         .config("spark.driver.host", "localhost")
         .config("spark.sql.execution.arrow.pyspark.enabled", "true")
         .config("spark.kryoserializer.buffer.max", "256m")
+        .config("spark.files.overwrite", "true")
         .master("local[*]")
         .enableHiveSupport()
         .getOrCreate()
diff --git a/replay/utils.py b/replay/utils.py
index e49994b2d..125ad4590 100644
--- a/replay/utils.py
+++ b/replay/utils.py
@@ -1,5 +1,7 @@
 import collections
 import pickle
+from dataclasses import dataclass
+from enum import Enum
 from typing import Any, Iterable, List, Optional, Set, Tuple, Union
 
 import numpy as np
@@ -396,7 +398,7 @@ def to_csr(
         return csr_matrix(
             (
                 [],
-                ([],[]),
+                ([], []),
             ),
             shape=(0, 0),
         )
@@ -739,6 +741,101 @@ def drop_temp_view(temp_view_name: str) -> None:
     spark.catalog.dropTempView(temp_view_name)
 
 
+class FileSystem(Enum):
+    """File system types"""
+    HDFS = 1
+    LOCAL = 2
+
+
+def get_default_fs() -> str:
+    """Returns hadoop `fs.defaultFS` property value"""
+    spark = SparkSession.getActiveSession()
+    hadoop_conf = spark._jsc.hadoopConfiguration()
+    default_fs = hadoop_conf.get("fs.defaultFS")
+    return default_fs
+
+
+@dataclass(frozen=True)
+class FileInfo:
+    """File meta-information: filesystem, path and hdfs_uri (optional)"""
+    path: str
+    filesystem: FileSystem
+    hdfs_uri: str = None
+
+
+def get_filesystem(path: str) -> FileInfo:
+    """Analyzes path and hadoop config and return `FileInfo` instance with `filesystem`,
+    `hdfs uri` (if filesystem is hdfs) and `cleaned path` (without prefix).
+
+    For example:
+
+    >>> path = 'hdfs://node21.bdcl:9000/tmp/file'
+    >>> get_filesystem(path)
+    FileInfo(path='/tmp/file', filesystem=<FileSystem.HDFS: 1>, hdfs_uri='hdfs://node21.bdcl:9000')
+
+    >>> path = 'file:///tmp/file'
+    >>> get_filesystem(path)
+    FileInfo(path='/tmp/file', filesystem=<FileSystem.LOCAL: 2>, hdfs_uri=None)
+
+    >>> spark = SparkSession.builder.master("local[1]").getOrCreate()
+    >>> path = 'hdfs:///tmp/file'
+    >>> get_filesystem(path)
+    Traceback (most recent call last):
+     ...
+    ValueError: Can't get default hdfs uri for path = 'hdfs:///tmp/file'. \
+Specify an explicit path, such as 'hdfs://host:port/dir/file', \
+or set 'fs.defaultFS' in hadoop configuration.
+
+    >>> path = '/tmp/file'
+    >>> get_filesystem(path)
+    FileInfo(path='/tmp/file', filesystem=<FileSystem.LOCAL: 2>, hdfs_uri=None)
+
+    >>> spark = SparkSession.builder.master("local[1]").getOrCreate().newSession()
+    >>> spark.sparkContext._jsc.hadoopConfiguration().set('fs.defaultFS', 'hdfs://node21.bdcl:9000')
+    >>> path = '/tmp/file'
+    >>> get_filesystem(path)
+    FileInfo(path='/tmp/file', filesystem=<FileSystem.HDFS: 1>, hdfs_uri='hdfs://node21.bdcl:9000')
+
+    >>> path = 'hdfs:///tmp/file'
+    >>> get_filesystem(path)
+    FileInfo(path='/tmp/file', filesystem=<FileSystem.HDFS: 1>, hdfs_uri='hdfs://node21.bdcl:9000')
+
+    Return fs.defaultFS value because the current session may be used in another test
+    >>> spark.sparkContext._jsc.hadoopConfiguration().set('fs.defaultFS', 'file:///')
+
+
+    Args:
+        path (str): path to file on hdfs or local disk
+
+    Returns: FileInfo instance: `filesystem id`,
+    `hdfs uri` (if filesystem is hdfs) and `cleaned path` (without prefix)
+    """
+    prefix_len = 7  # 'hdfs://' and 'file://' length
+    if path.startswith("hdfs://"):
+        if path.startswith("hdfs:///"):
+            default_fs = get_default_fs()
+            if default_fs.startswith("hdfs://"):
+                return FileInfo(path[prefix_len:], FileSystem.HDFS, default_fs)
+            else:
+                raise ValueError(
+                    f"Can't get default hdfs uri for path = '{path}'. "
+                    "Specify an explicit path, such as 'hdfs://host:port/dir/file', "
+                    "or set 'fs.defaultFS' in hadoop configuration."
+                )
+        else:
+            hostname = path[prefix_len:].split("/", 1)[0]
+            hdfs_uri = "hdfs://" + hostname
+            return FileInfo(path[len(hdfs_uri) :], FileSystem.HDFS, hdfs_uri)
+    elif path.startswith("file://"):
+        return FileInfo(path[prefix_len:], FileSystem.LOCAL)
+    else:
+        default_fs = get_default_fs()
+        if default_fs.startswith("hdfs://"):
+            return FileInfo(path, FileSystem.HDFS, default_fs)
+        else:
+            return FileInfo(path, FileSystem.LOCAL)
+
+
 def sample_top_k_recs(pairs: DataFrame, k: int, seed: int = None):
     """
     Sample k items for each user with probability proportional to the relevance score.
diff --git a/tests/models/test_als.py b/tests/models/test_als.py
index 67da10cab..5532a2716 100644
--- a/tests/models/test_als.py
+++ b/tests/models/test_als.py
@@ -4,6 +4,13 @@
 
 from pyspark.sql import functions as sf
 
+from replay.ann.entities.hnswlib_param import HnswlibParam
+from replay.ann.index_builders.executor_hnswlib_index_builder import (
+    ExecutorHnswlibIndexBuilder,
+)
+from replay.ann.index_stores.shared_disk_index_store import (
+    SharedDiskIndexStore,
+)
 from replay.models import ALSWrap
 from replay.scenarios.two_stages.two_stages_scenario import (
     get_first_level_model_features,
@@ -18,6 +25,28 @@ def model():
     return model
 
 
+@pytest.fixture
+def model_with_ann(tmp_path):
+    model = ALSWrap(
+        rank=2,
+        implicit_prefs=False,
+        seed=42,
+        index_builder=ExecutorHnswlibIndexBuilder(
+            index_params=HnswlibParam(
+                space="ip",
+                m=100,
+                ef_c=2000,
+                post=0,
+                ef_s=2000,
+            ),
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path), index_dir="hnswlib_index"
+            ),
+        ),
+    )
+    return model
+
+
 def test_works(log, model):
     try:
         pred = model.fit_predict(log, k=1)
@@ -65,3 +94,23 @@ def test_enrich_with_features(log, model):
         [row_dict["_if_1"], row_dict["_if_1"] * row_dict["_uf_1"]],
         [-2.938199281692505, 0],
     )
+
+
+@pytest.mark.parametrize(
+    "filter_seen_items", [True, False]
+)
+def test_ann_predict(log, model, model_with_ann, filter_seen_items):
+    model.fit(log)
+    recs1 = model.predict(log, k=1, filter_seen_items=filter_seen_items)
+
+    model_with_ann.fit(log)
+    recs2 = model_with_ann.predict(log, k=1, filter_seen_items=filter_seen_items)
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert recs1.user_idx.equals(recs2.user_idx)
+    assert recs1.item_idx.equals(recs2.item_idx)
diff --git a/tests/models/test_distributed_lightfm_wrap.py b/tests/models/test_distributed_lightfm_wrap.py
new file mode 100644
index 000000000..5e5419115
--- /dev/null
+++ b/tests/models/test_distributed_lightfm_wrap.py
@@ -0,0 +1,225 @@
+# pylint: disable-all
+from datetime import datetime
+
+import numpy as np
+import pytest
+from pyspark.sql import functions as sf
+
+from replay.ann.index_builders.driver_hnswlib_index_builder import DriverHnswlibIndexBuilder
+from replay.ann.index_stores.spark_files_index_store import SparkFilesIndexStore
+from replay.constants import LOG_SCHEMA
+from replay.models import DistributedLightFMWrap
+from replay.scenarios.two_stages.two_stages_scenario import (
+    get_first_level_model_features,
+)
+from replay.ann.entities.hnswlib_param import HnswlibParam
+
+from tests.utils import spark, log as log_hnswlib_pred
+
+
+@pytest.fixture
+def log(spark):
+    date = datetime(2019, 1, 1)
+    return spark.createDataFrame(
+        data=[
+            [0, 0, date, 1.0],
+            [1, 0, date, 1.0],
+            [2, 1, date, 2.0],
+            [2, 1, date, 2.0],
+            [1, 1, date, 2.0],
+            [2, 2, date, 2.0],
+            [0, 2, date, 2.0],
+        ],
+        schema=LOG_SCHEMA,
+    )
+
+
+@pytest.fixture
+def user_features(spark):
+    return spark.createDataFrame(
+        [(0, 2.0, 5.0), (1, 0.0, -5.0), (4, 4.0, 3.0)]
+    ).toDF("user_idx", "user_feature_1", "user_feature_2")
+
+
+@pytest.fixture
+def item_features(spark):
+    return spark.createDataFrame([(0, 4.0, 5.0), (1, 5.0, 4.0)]).toDF(
+        "item_idx", "item_feature_1", "item_feature_2"
+    )
+
+
+@pytest.fixture
+def model():
+    model = DistributedLightFMWrap(
+        no_components=1,
+        random_state=42,
+        loss="bpr",
+        tcp_port=1234,
+        connection_timeout=10,
+    )
+    model.num_epochs = 10
+    return model
+
+
+@pytest.fixture
+def model_ann():
+    model = DistributedLightFMWrap(
+        no_components=1,
+        random_state=42,
+        loss="bpr",
+        tcp_port=1234,
+        connection_timeout=10,
+        index_builder=DriverHnswlibIndexBuilder(
+            index_params=HnswlibParam(
+                space="ip",
+                m=100,
+                ef_c=2000,
+                post=0,
+                ef_s=2000,
+            ),
+            index_store=SparkFilesIndexStore(),
+        ),
+    )
+    model.num_epochs = 10
+    return model
+
+
+def test_predict(log, user_features, item_features, model):
+    model.fit(log, user_features, item_features)
+    pred = model.predict(
+        log=log,
+        k=1,
+        user_features=user_features,
+        item_features=item_features,
+        filter_seen_items=True,
+    )
+
+    assert list(pred.toPandas().sort_values("user_idx")["item_idx"]) == [1, 2, 0]
+
+
+def test_predict_ann(log_hnswlib_pred, user_features, item_features, model_ann):
+    model_ann.fit(log_hnswlib_pred, user_features, item_features)
+    pred = model_ann.predict(
+        log=log_hnswlib_pred,
+        k=1,
+        user_features=user_features,
+        item_features=item_features,
+        filter_seen_items=True,
+    )
+
+    assert list(pred.toPandas().sort_values("user_idx")["item_idx"]) == [3, 1, 3, 3]
+
+
+def test_predict_no_user_features(log, user_features, item_features, model):
+    model.fit(log, None, item_features)
+    assert model.can_predict_cold_items
+    assert not model.can_predict_cold_users
+    pred = model.predict(
+        log=log,
+        k=1,
+        user_features=None,
+        item_features=item_features,
+        filter_seen_items=True,
+    )
+    assert list(pred.toPandas().sort_values("user_idx")["item_idx"]) == [1, 2, 0]
+
+
+def test_predict_pairs(log, user_features, item_features, model):
+    try:
+        model.fit(
+            log.filter(sf.col("user_idx") != 0),
+            user_features.filter(sf.col("user_idx") != 0),
+            item_features,
+        )
+        pred = model.predict_pairs(
+            log.filter(sf.col("user_idx") == 0).select("user_idx", "item_idx"),
+            user_features=user_features,
+            item_features=item_features,
+        )
+        assert pred.count() == 2
+        assert pred.select("user_idx").distinct().collect()[0][0] == 0
+        pred = model.predict_pairs(
+            log.filter(sf.col("user_idx") == 1).select("user_idx", "item_idx"),
+            user_features=user_features,
+            item_features=item_features,
+        )
+        assert pred.count() == 2
+        assert pred.select("user_idx").distinct().collect()[0][0] == 1
+    except:  # noqa
+        pytest.fail()
+
+
+def test_raises_fit(log, user_features, item_features, model):
+    with pytest.raises(ValueError, match="features for .*"):
+        model.fit(
+            log.filter(sf.col("user_idx") != 0),
+            user_features.filter(sf.col("user_idx") != 1),
+            item_features,
+        )
+
+
+def test_raises_predict(log, user_features, item_features, model):
+    with pytest.raises(
+            ValueError, match="Item features are missing for predict"
+    ):
+        model.fit(log, None, item_features)
+        pred = model.predict_pairs(
+            log.select("user_idx", "item_idx"),
+            user_features=None,
+            item_features=None,
+        )
+
+
+def _fit_predict_compare_features(
+        model, log, user_features, user_features_filtered, item_features, test_ids
+):
+    model.fit(
+        log, user_features=user_features_filtered, item_features=item_features
+    )
+
+    pred_for_test = (
+        model.predict_pairs(
+            test_ids.select("user_idx", "item_idx"),
+            log,
+            user_features=user_features,
+            item_features=item_features,
+        )
+        .select("relevance")
+        .collect()[0][0]
+    )
+    row_dict = (
+        get_first_level_model_features(
+            model,
+            test_ids,
+            user_features=user_features,
+            item_features=item_features,
+        )
+        .collect()[0]
+        .asDict()
+    )
+    assert np.isclose(
+        row_dict["_if_0"] * row_dict["_uf_0"]
+        + row_dict["_user_bias"]
+        + row_dict["_item_bias"],
+        pred_for_test,
+    )
+
+
+def test_enrich_with_features(log, user_features, item_features, model):
+    test_pair = log.filter(
+        (sf.col("item_idx") == 1) & (sf.col("user_idx") == 1)
+    )
+
+    for user_f, item_f in [[None, None], [user_features, item_features]]:
+        _fit_predict_compare_features(
+            model, log, user_f, user_f, item_f, test_pair
+        )
+        if item_f is not None:
+            _fit_predict_compare_features(
+                model,
+                log.filter(sf.col("user_idx") != 1),
+                user_f,
+                user_f.filter(sf.col("user_idx") != 1),
+                item_f,
+                test_pair,
+            )
diff --git a/tests/models/test_knn.py b/tests/models/test_knn.py
index f44c7418c..2447369e7 100644
--- a/tests/models/test_knn.py
+++ b/tests/models/test_knn.py
@@ -4,6 +4,13 @@
 import pytest
 import numpy as np
 
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+from replay.ann.index_builders.driver_nmslib_index_builder import (
+    DriverNmslibIndexBuilder,
+)
+from replay.ann.index_stores.spark_files_index_store import (
+    SparkFilesIndexStore,
+)
 from replay.constants import LOG_SCHEMA
 from replay.models import ItemKNN
 from tests.utils import spark
@@ -23,6 +30,22 @@ def log(spark):
     )
 
 
+@pytest.fixture
+def log_2items_per_user(spark):
+    date = datetime(2019, 1, 1)
+    return spark.createDataFrame(
+        data=[
+            [0, 0, date, 1.0],
+            [0, 1, date, 1.0],
+            [1, 0, date, 1.0],
+            [1, 1, date, 1.0],
+            [2, 2, date, 1.0],
+            [2, 3, date, 1.0],
+        ],
+        schema=LOG_SCHEMA,
+    )
+
+
 @pytest.fixture
 def weighting_log(spark):
     date = datetime(2019, 1, 1)
@@ -44,6 +67,24 @@ def model():
     return model
 
 
+@pytest.fixture
+def model_with_ann(tmp_path):
+    nmslib_hnsw_params = NmslibHnswParam(
+        space="negdotprod_sparse",
+        m=10,
+        ef_s=200,
+        ef_c=200,
+        post=0,
+    )
+    return ItemKNN(
+        1,
+        weighting=None,
+        index_builder=DriverNmslibIndexBuilder(
+            index_params=nmslib_hnsw_params, index_store=SparkFilesIndexStore()
+        ),
+    )
+
+
 @pytest.fixture
 def tf_idf_model():
     model = ItemKNN(1, weighting="tf_idf")
@@ -65,15 +106,9 @@ def test_works(log, model):
 
 def test_tf_idf(weighting_log, tf_idf_model):
     idf = tf_idf_model._get_idf(weighting_log).toPandas()
-    assert np.allclose(
-        idf[idf["user_idx"] == 1]["idf"], np.log1p(2 / 1)
-    )
-    assert np.allclose(
-        idf[idf["user_idx"] == 0]["idf"], np.log1p(2 / 2)
-    )
-    assert np.allclose(
-        idf[idf["user_idx"] == 2]["idf"], np.log1p(2 / 2)
-    )
+    assert np.allclose(idf[idf["user_idx"] == 1]["idf"], np.log1p(2 / 1))
+    assert np.allclose(idf[idf["user_idx"] == 0]["idf"], np.log1p(2 / 2))
+    assert np.allclose(idf[idf["user_idx"] == 2]["idf"], np.log1p(2 / 2))
 
     tf_idf_model.fit(weighting_log)
     recs = tf_idf_model.predict(weighting_log, k=1, users=[0, 1]).toPandas()
@@ -118,3 +153,39 @@ def test_weighting_raises(log, tf_idf_model):
     with pytest.raises(ValueError, match="weighting must be one of .*"):
         tf_idf_model.weighting = " "
         log = tf_idf_model._reweight_log(log)
+
+
+def test_knn_predict_filter_seen_items(log, model, model_with_ann):
+    model.fit(log)
+    recs1 = model.predict(log, k=1, filter_seen_items=True)
+
+    model_with_ann.fit(log)
+    recs2 = model_with_ann.predict(log, k=1, filter_seen_items=True)
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert recs1.user_idx.equals(recs2.user_idx)
+    assert recs1.item_idx.equals(recs2.item_idx)
+
+
+def test_knn_predict(log_2items_per_user, model, model_with_ann):
+    model.fit(log_2items_per_user)
+    recs1 = model.predict(log_2items_per_user, k=2, filter_seen_items=False)
+
+    model_with_ann.fit(log_2items_per_user)
+    recs2 = model_with_ann.predict(
+        log_2items_per_user, k=2, filter_seen_items=False
+    )
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert all(recs1.user_idx.values == recs2.user_idx.values)
+    assert all(recs1.item_idx.values == recs2.item_idx.values)
diff --git a/tests/models/test_save_load_models.py b/tests/models/test_save_load_models.py
index cc06b820a..d4641fbb5 100644
--- a/tests/models/test_save_load_models.py
+++ b/tests/models/test_save_load_models.py
@@ -9,6 +9,25 @@
 from pyspark.sql import functions as sf
 
 import replay
+from replay.ann.entities.hnswlib_param import HnswlibParam
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+from replay.ann.index_builders.driver_hnswlib_index_builder import DriverHnswlibIndexBuilder
+from replay.ann.index_builders.driver_nmslib_index_builder import (
+    DriverNmslibIndexBuilder,
+)
+from replay.ann.index_builders.executor_hnswlib_index_builder import (
+    ExecutorHnswlibIndexBuilder,
+)
+from replay.ann.index_builders.executor_nmslib_index_builder import (
+    ExecutorNmslibIndexBuilder,
+)
+from replay.ann.index_stores.hdfs_index_store import HdfsIndexStore
+from replay.ann.index_stores.shared_disk_index_store import (
+    SharedDiskIndexStore,
+)
+from replay.ann.index_stores.spark_files_index_store import (
+    SparkFilesIndexStore,
+)
 from replay.data_preparator import Indexer
 from replay.model_handler import save, load
 from replay.models import *
@@ -156,3 +175,121 @@ def test_study(df, tmp_path):
     save(model, path)
     loaded_model = load(path)
     assert loaded_model.study == model.study
+
+
+def test_ann_als_saving_loading(long_log_with_features, tmp_path):
+    model = ALSWrap(
+        rank=2,
+        implicit_prefs=False,
+        seed=42,
+        index_builder=ExecutorHnswlibIndexBuilder(
+            index_params=HnswlibParam(
+                space="ip",
+                m=100,
+                ef_c=2000,
+                post=0,
+                ef_s=2000,
+            ),
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path),
+                index_dir="hnswlib_index",
+                cleanup=False,
+            ),
+        ),
+    )
+
+    path = (tmp_path / "test").resolve()
+    model.fit(long_log_with_features)
+    base_pred = model.predict(long_log_with_features, 5)
+    save(model, path)
+    loaded_model = load(path)
+    new_pred = loaded_model.predict(long_log_with_features, 5)
+    sparkDataFrameEqual(base_pred, new_pred)
+
+
+def test_ann_word2vec_saving_loading(long_log_with_features, tmp_path):
+    model = Word2VecRec(
+        rank=1, window_size=1, use_idf=True, seed=42, min_count=0,
+        index_builder=DriverHnswlibIndexBuilder(
+            index_params=HnswlibParam(
+                space="l2",
+                m=100,
+                ef_c=2000,
+                post=0,
+                ef_s=2000,
+            ),
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path),
+                index_dir="hnswlib_index"
+            )
+        )
+    )
+
+    path = (tmp_path / "test").resolve()
+    model.fit(long_log_with_features)
+    base_pred = model.predict(long_log_with_features, 5)
+    save(model, path)
+    loaded_model = load(path)
+    new_pred = loaded_model.predict(long_log_with_features, 5)
+    sparkDataFrameEqual(base_pred, new_pred)
+
+
+def test_ann_slim_saving_loading(long_log_with_features, tmp_path):
+    nmslib_hnsw_params = NmslibHnswParam(
+        space="negdotprod_sparse",
+        m=10,
+        ef_s=200,
+        ef_c=200,
+        post=0,
+    )
+    model = SLIM(
+        0.0,
+        0.01,
+        seed=42,
+        index_builder=DriverNmslibIndexBuilder(
+            index_params=nmslib_hnsw_params,
+            index_store=SparkFilesIndexStore(),
+        ),
+    )
+
+    path = (tmp_path / "test").resolve()
+    model.fit(long_log_with_features)
+    base_pred = model.predict(long_log_with_features, 5)
+    save(model, path)
+    loaded_model = load(path)
+    new_pred = loaded_model.predict(long_log_with_features, 5)
+    sparkDataFrameEqual(base_pred, new_pred)
+
+
+def test_ann_knn_saving_loading(long_log_with_features, tmp_path):
+    nmslib_hnsw_params = NmslibHnswParam(
+        space="negdotprod_sparse",
+        m=10,
+        ef_s=200,
+        ef_c=200,
+        post=0,
+    )
+    model = ItemKNN(
+        1,
+        weighting=None,
+        index_builder=ExecutorNmslibIndexBuilder(
+            index_params=nmslib_hnsw_params,
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path), index_dir="nmslib_hnsw_index"
+            ),
+        ),
+    )
+
+    path = (tmp_path / "test").resolve()
+    model.fit(long_log_with_features)
+    base_pred = model.predict(long_log_with_features, 5)
+    save(model, path)
+    loaded_model = load(path)
+    new_pred = loaded_model.predict(long_log_with_features, 5)
+    sparkDataFrameEqual(base_pred, new_pred)
+
+
+def test_hdfs_index_store_exception():
+    local_warehouse_dir = 'file:///tmp'
+    with pytest.raises(ValueError, match=f"Can't recognize path {local_warehouse_dir + '/index_dir'} as HDFS path!"):
+        HdfsIndexStore(warehouse_dir=local_warehouse_dir, index_dir="index_dir")
diff --git a/tests/models/test_slim.py b/tests/models/test_slim.py
index 384ca64c9..a6aa014c6 100644
--- a/tests/models/test_slim.py
+++ b/tests/models/test_slim.py
@@ -3,6 +3,13 @@
 import pytest
 import numpy as np
 
+from replay.ann.entities.nmslib_hnsw_param import NmslibHnswParam
+from replay.ann.index_builders.executor_nmslib_index_builder import (
+    ExecutorNmslibIndexBuilder,
+)
+from replay.ann.index_stores.shared_disk_index_store import (
+    SharedDiskIndexStore,
+)
 from replay.models import SLIM
 from tests.utils import log, spark
 
@@ -12,6 +19,28 @@ def model():
     return SLIM(0.0, 0.01, seed=42)
 
 
+@pytest.fixture
+def model_with_ann(tmp_path):
+    nmslib_hnsw_params = NmslibHnswParam(
+        space="negdotprod_sparse",
+        m=10,
+        ef_s=200,
+        ef_c=200,
+        post=0,
+    )
+    return SLIM(
+        0.0,
+        0.01,
+        seed=42,
+        index_builder=ExecutorNmslibIndexBuilder(
+            index_params=nmslib_hnsw_params,
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path), index_dir="nmslib_hnsw_index"
+            ),
+        ),
+    )
+
+
 def test_fit(log, model):
     model.fit(log)
     assert np.allclose(
@@ -42,6 +71,23 @@ def test_predict(log, model):
     )
 
 
+def test_ann_predict(log, model, model_with_ann):
+    model.fit(log)
+    recs1 = model.predict(log, k=1)
+
+    model_with_ann.fit(log)
+    recs2 = model_with_ann.predict(log, k=1)
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert recs1.user_idx.equals(recs2.user_idx)
+    assert recs1.item_idx.equals(recs2.item_idx)
+
+
 @pytest.mark.parametrize(
     "beta,lambda_", [(0.0, 0.0), (-0.1, 0.1), (0.1, -0.1)]
 )
diff --git a/tests/models/test_word2vec.py b/tests/models/test_word2vec.py
index 1a8c08e1a..a0693475c 100644
--- a/tests/models/test_word2vec.py
+++ b/tests/models/test_word2vec.py
@@ -5,10 +5,13 @@
 import numpy as np
 from pyspark.sql import functions as sf
 
+from replay.ann.entities.hnswlib_param import HnswlibParam
+from replay.ann.index_builders.driver_hnswlib_index_builder import DriverHnswlibIndexBuilder
+from replay.ann.index_stores.shared_disk_index_store import SharedDiskIndexStore
 from replay.constants import LOG_SCHEMA
 from replay.models import Word2VecRec
 from replay.utils import vector_dot
-from tests.utils import spark
+from tests.utils import spark, log as log2
 
 
 @pytest.fixture
@@ -35,6 +38,27 @@ def model():
     )
 
 
+@pytest.fixture
+def model_with_ann(tmp_path):
+    model = Word2VecRec(
+        rank=1, window_size=1, use_idf=True, seed=42, min_count=0,
+        index_builder=DriverHnswlibIndexBuilder(
+            index_params=HnswlibParam(
+                space="ip",
+                m=100,
+                ef_c=2000,
+                post=0,
+                ef_s=2000,
+            ),
+            index_store=SharedDiskIndexStore(
+                warehouse_dir=str(tmp_path),
+                index_dir="hnswlib_index"
+            )
+        )
+    )
+    return model
+
+
 def test_fit(log, model):
     model.fit(log)
     vectors = (
@@ -60,3 +84,38 @@ def test_predict(log, model):
         recs.toPandas().sort_values("user_idx").relevance,
         [1.0003180271011836, 0.9653348251181987, 0.972993367280087],
     )
+
+
+# here we use `test.utils.log` because we can't build the hnsw index on `log` data
+def test_word2vec_predict_filter_seen_items(log2, model, model_with_ann):
+    model.fit(log2)
+    recs1 = model.predict(log2, k=1)
+
+    model_with_ann.fit(log2)
+    recs2 = model_with_ann.predict(log2, k=1)
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert recs1.user_idx.equals(recs2.user_idx)
+    assert recs1.item_idx.equals(recs2.item_idx)
+
+
+def test_word2vec_predict(log2, model, model_with_ann):
+    model.fit(log2)
+    recs1 = model.predict(log2, k=2, filter_seen_items=False)
+
+    model_with_ann.fit(log2)
+    recs2 = model_with_ann.predict(log2, k=2, filter_seen_items=False)
+
+    recs1 = recs1.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    recs2 = recs2.toPandas().sort_values(
+        ["user_idx", "item_idx"], ascending=False
+    )
+    assert recs1.user_idx.equals(recs2.user_idx)
+    assert recs1.item_idx.equals(recs2.item_idx)