Machine Learning Algorithms

Content-based systems

This is probably the simplest method and it's based only on the products, modeled as feature vectors:

Just like the users, the features can also be categorical (indeed, for products it's easier), for example, the genre of a book or a movie, and they can be used together with numerical values (like price, length, number of positive reviews, and so on) after encoding them.

Then a clustering strategy is adopted, even if the most used is k-nearest neighbors as it allows controlling the size of each neighborhood to determine, given a sample product, the quality and the number of suggestions.

Using scikit-learn, first of all ...