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Collaborative Filtering Deep Dive

One very common problem to solve is when you have a number of users and a number of products, and you want to recommend which products are most likely to be useful for which users. There are many variations of this: for example, recommending movies (such as on Netflix), figuring out what to highlight for a user on a home page, deciding what stories to show in a social media feed, and so forth. There is a general solution to this problem, called collaborative filtering, which works like this: look at what products the current user has used or liked, find other users that have used or liked similar products, and then recommend other products that those users have used or liked.

There is actually a more general class of problems that this approach can solve, not necessarily involving users and products. Indeed, for collaborative filtering we more commonly refer to items, rather than products. Items could be links that people click on, diagnoses that are selected for patients, and so forth.

For this chapter we are going to work on this movie recommendation problem. We’ll start by getting some data suitable for a collaborative filtering model.