Hands-On Machine Learning with C++

User-based collaborative filtering

This class of system began to develop in the '90s. Under this approach, recommendations are generated based on the interests of other similar users. Such recommendations are the result of the collaboration of many users, hence the name of the method.

The classical implementation of the algorithm is based on the principle of k-nearest neighbors (kNN). For every user, we look for the k most similar to them (in terms of preferences). Then, we supplement the information about the user with known data from their neighbors. So, for example, if it is known that your neighbors are delighted with a movie, and you have not watched it for some reason, this is a great reason to recommend this movie.

The similarity is, ...