A Latent Factor Recommendation system that we can use to recommend movies to users.
It is a simple Implementation of Stochastic Gradient Descent algorithm to build a Latent Factor Recommendation system. Using PySpark would be a good idea, as the data is going to be huge, it would be easy to process the data in parallel across a cluster.
ratings.csv is the matrix R. Each entry is a movie id, user id, and a rating that is an integer between 1 and 5. It has around 609 users and 9485 movies. As you can see, it is a small dataset I have used NumPy.
We are given a matrix R of recommendations. The element Riu of this matrix corresponds to the rating given by user u to item i. The size of R is m × n, where m is the number of movies and n the number of users. Most of the elements of the matrix are unknown because each user can only rate a few movies. Our goal is to find two matrices, P and Q, such that R = QPT. The dimensions of Q are m × k, and the dimensions of P are n × k. k is a parameter of the algorithm.