Hands-On Machine Learning with C++

Item-based collaborative filtering

The item-based approach is a natural alternative to the classic user-based approach described previously, and almost repeats it, except for one thing—it applies to the transposed preference matrix, which looks for similar products instead of users.

For each client, a user-based collaborative filtering system searches a group of customers who are similar to this user in terms of previous purchases, and then the system averages their preferences. These average preferences serve as recommendations for the user. In the case of item-based collaborative filtering, the nearest neighbors are searched for on a variety of products (items)—columns of a preference matrix, and the averaging occurs precisely according ...