Deep Learning for Coders with fastai and PyTorch

Chapter 8. Collaborative Filtering Deep Dive

One common problem to solve is having 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. Many variations exist: 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. A general solution to this problem, called collaborative filtering, works like this: look at which products the current user has used or liked, find other users who have used or liked similar products, and then recommend other products that those users have used or liked.

For example, on Netflix, you may have watched lots of movies that are science fiction, full of action, and were made in the 1970s. Netflix may not know these particular properties of the films you have watched, but it will be able to see that other people who have watched the same movies that you watched also tended to watch other movies that are science fiction, full of action, and were made in the 1970s. In other words, to use this approach, we don’t necessarily need to know anything about the movies except who likes to watch them.

There is 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, diagnoses that are selected ...