Online streaming platforms like Netflix have plenty of movies in their repository and if one can build a Recommendation System to recommend relevant movies to users, based on their historical interactions, this would improve customer satisfaction and hence, improve the revenue of the platform. The techniques used here will not only be limited to movies, and can be used for any recommendation system.
In this project I built various recommendation systems:
- Knowledge/Rank based recommendation system
- Similarity-Based Collaborative filtering
- Matrix Factorization Based Collaborative Filtering
This was achieved by using the ratings dataset.