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.

Outline

1. Exploring the MovieLens Data
   
2. Preliminaries
   
3. Training a matrix factorization model
   
4. Regularization in matrix factorization
   
5. Get recommendations for users

Other Included files

• A Conda environment file containing all the dependecies required to run and execute this system