Online streaming platforms like Netflix have plenty of movies in their repository and if we can build a Recommendation System to recommend relevant movies to users, based on their historical interactions, this would improve customer satisfaction and hence, it will also improve the revenue of the platform. The techniques that we will learn here will not only be limited to movies, it can be any item for which you want to build a recommendation system.
In this project we will be building various recommendation systems:
- Knowledge/Rank based recommendation system
- Similarity-Based Collaborative filtering
- Matrix Factorization Based Collaborative Filtering
we are going to use the ratings dataset.
The ratings dataset contains the following attributes:
- userId
- movieId
- rating
- timestamp