SyedMa3 / ncf

Implementation of Neural Collaborative Filtering

Neural Collaborative Filtering

This is an implementation of Neural Collaborative Filtering for a movie recommendation system based on the MovieLens 1M Dataset.

References:

Neural Collaborative Filtering

I wanted to build a movie recommendation system and use deep learning for it. So I came across NCF and it seemed like a good starting point so I implemented it.

To tackle the cold-start problem, we can ask the users to mention a few movies he liked and then proceed.

Evaluation Protocols:

To evaluate the performance of item recommendation, we use the leave-one-out evaluation.

Since it is too time consuming to rate every non-interacted movie, we take random 100 movies and display the top 10 movies for that user.

Structure of Application:

• dataset.py - Split the dataset into train and test and turned data into implicit feedback.
• neuMF.ipynb - Train the model and predict 10 movies for a given user.
• model.h5 - Saved model so that I can fine-tune on new users.