Let's look at how we can implement a collaborative filtering recommender system. As a sample dataset for this example, we use the MovieLens dataset provided by GroupLens from the research lab in the Department of Computer Science and Engineering at the University of Minnesota: https://grouplens.org/datasets/movielens/. They provide a full dataset with 20 million movie ratings and a smaller one for education, with 100,000 ratings. We recommend starting with the smaller one because it allows us to see results earlier and detect implementation errors faster.

This dataset consists of several files, but we are only interested in two of them: ratings.csv and movies.csv. The rating file contains lines with the following format: the user ID, the movie ID, the rating, and the timestamp. In this dataset, users made ratings on a 5-star scale, with half-star increments (0.5 stars—5.0 stars). The movie's file contains lines with the following format: the movie ID, the title, and the genre. The movie ID is the same in both files, so we can see which movies users are rating.