Machine Learning with Spark - Second Edition

Collaborative filtering

Collaborative filtering relies only on past behavior, such as previous ratings or transactions. The idea behind this is the notion of similarity.

The basic idea is that the user gives ratings to items, implicitly or explicitly. Users who had a similar taste in the past will have a similar taste in the future.

In a user-based approach, if two users have exhibited similar preferences, that is, patterns of interacting with the same items in broadly the same way, then we would assume that they are similar to each other in terms of taste. To generate recommendations for unknown items for a given user, we can use the known preferences of other users that exhibit similar behavior. We can do this by selecting a set of similar ...