TOYOKOTO - Anime Recommendation System

Viewers often waste hours scrolling through hundreds, if not thousands, of anime episodes before finding anything they enjoy. In order to build a better streaming environment that promotes income and increases the time spent on a website, businesses must be given advice based on their likes and wants.

Objective

We use machine learning to analyse the information in order to construct a recommendation system that can assist someone in deciding what to watch next.

Dataset

The dataset is downloaded fron Kaggle. The dataset contains two CSV files anime.csv and ratings.csv

Link to dataset : Download

Problem Statement

Using an anime dataset, to create a recommendation system based on two famous algorithms:

content based filtering

collaborative filtering

Technologies

Used Python libraries such as streamlit, numpy, sklearn, pandas , cosine similarity

Machine Learning Models

Content Based Filtering

User Based Filtering

Item Based Filtering

Video Demo

Click here

Website UI

Guide for your System

Clone the git repository to your system

Download the Dataset from Kaggle. Download

Move the Downloaded rating.csv and anime.csv to the folder in which you have cloned the repository.

upload the Anime-Recommendation-system.ipynb on the Jupyter Notebook. Run the project.

open the app.py file in IDE , open the terminal and run pip install streamlit

The construction of anime name.pkl and similarity.pkl will occur when running the Jupyter Notebook code. Those files must be copied and pasted into the same folder as app.py

then app.py should be executed using the streamlit run app.py command.

Algorithms

Content Based Recommendation System

This algorithm recommends products which are similar to the ones that a user has liked in the past. The content of each item is represented as a set of descriptors or terms, typically the words that occur in a document.A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate

Here, there are various approaches to solve this problem and I have applied them keeping in mind their usage in theindustry. They are broadly based on 2 category: 1) User Based: Takes user_id to get recommendation 2) Item Based: Give recommendation based on the search We use cosine similarity to find the relation

Cosine similarity metric measures the cosine of the angle between two n-dimensional vectors projected in a multi-dimensional space. The Cosine similarity of two documents will range from 0 to 1.

Collaborative Filtering Recommender System

Collaborative filtering is a technique that can filter out items that a user might like on the basis of reactions by similar users. It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user. 1) User Based collaborative filtering- This algorithm first finds the similarity score between users. Based on this similarity score, it then picks out the most similar users and recommends products which these similar users have liked or bought previously. 2) Item Based collaborative filtering: In this algorithm, we compute the similarity between each pair of items. So in our case we will find the similarity between each anime pair and based on that, we will recommend similar animes which are liked by the users in the past.

Conclusion/Discussion

A major drawback of content based filtering algorithm is that it is limited to recommending items that are of the same type. It will never recommend products which the user has not bought or liked in the past. So if a user has watched or liked only action movies in the past, the system will recommend only action movies. It’s a very narrow way of building an engine. To improve on this type of system, we need an algorithm that can recommend items not just based on the content, but the behavior of users as well.

In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about challenges in the performance of the recommendation.One typical problem caused by the data sparsity is the cold start problem. As collaborative filtering methods recommend items based on users' past preferences, new users will need to rate a sufficient number of items to enable the system to capture their preferences accurately and thus provides reliable recommendations.

Similarly, new items also have the same problem. When new items are added to the system, they need to be rated by a substantial number of users before they could be recommended to users who have similar tastes to the ones who rated them. The new item problem does not affect content-based recommendations, because the recommendation of an item is based on its discrete set of descriptive qualities rather than its ratings.

Future Possible Scope

Creating a hybrid recommendation system that that combines content-based filtering and collaborative filtering could potentially take advantage from both the representation of the content as well as the similarities among users and negates the limitations.

Improving the Frontend UI of the website

NOTE: The theme of the website might be changed when you will execute the code from your system

I hope you have liked the project!