This repo covers a Movie recommendation system using Collaborative filtering to learn Movie and User Embeddings.
It uses the MovieLens Dataset which consists of movie rating from 1 to 5.
Dataset: 943 users, 1900 Movies, 100,000 Unique Ratings.
- Exploring the MovieLens Data
- Preliminaries
- Training a matrix factorization model
- Regularization in matrix factorization
- Get recommendations for users
- A Conda environment file containing all the dependecies required to run and execute this system