diff --git a/tensorflow_ranking/python/model.py b/tensorflow_ranking/python/model.py
index ea8a7f3..ce50162 100644
--- a/tensorflow_ranking/python/model.py
+++ b/tensorflow_ranking/python/model.py
@@ -50,6 +50,20 @@ def _get_params(mode, params):
     raise ValueError('Invalid mode: {}.'.format(mode))
   return num_shuffles
 
+def _get_params_shuffle_peritem(mode, params):
+  params = params or {}
+  # 'shuffle_peritem' should be bool
+  _SHUFFLE_PERITEM = 'shuffle_peritem'
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    shuffle_peritem = bool(params.get(_SHUFFLE_PERITEM, None))
+  elif mode == tf.estimator.ModeKeys.EVAL:
+    shuffle_peritem = False
+  elif mode == tf.estimator.ModeKeys.PREDICT:
+    shuffle_peritem = False
+  else:
+    raise ValueError('Invalid mode: {}.'.format(mode))
+  return shuffle_peritem
+
 
 class _RankingModel(object):
   """Interface for a ranking model."""
@@ -335,6 +349,32 @@ def _update_scatter_gather_indices(self, is_valid, mode, params):
 
   def _compute_logits_impl(self, context_features, example_features, labels,
                            mode, params, config):
+    if _get_params_shuffle_peritem(mode, params):
+      with tf.compat.v1.name_scope("shuffle_peritem"):
+        # Shuffle labels and example features along list_size
+        # example_features are shape (batch, list_size, feature_space)
+        
+        first_example = next(iter(example_features.values()))
+        cur_list_size = tf.shape(input=first_example)[1]
+        
+        indicies = tf.range(start=0, limit=cur_list_size, dtype=tf.int32)
+        shuffled_indicies = tf.random.shuffle(indicies)
+        
+        for name, value in six.iteritems(example_features):
+          # Transpose to expose LIST_SIZE dimension on the 0th axis
+          transposed = tf.transpose(value, perm=[1,0,2])
+          
+          # Shuffle along the new LIST_SIZE axis
+          shuffled_feature = tf.gather(transposed, shuffled_indicies)
+          
+          # Revert back to (Batch, LIST_SIZE, feature_space)
+          reverted = tf.transpose(shuffled_feature, perm=[1,0,2])
+          example_features[name] = reverted
+          
+        transposed_label = tf.transpose(labels, perm=[1,0])
+        shuffled_label = tf.gather(transposed_label, shuffled_indicies)
+        labels = tf.transpose(shuffled_label, perm=[1,0])
+    
     # Scatter/Gather per-example scores through groupwise comparison. Each
     # instance in a mini-batch will form a number of groups. Each group of
     # examples are scored by `_score_fn` and scores for individual examples are