Hands-On Machine Learning with C++

An overview of recommender system algorithms

A recommender system's task is to inform a user about an object that could be the most interesting to them at a given time. Most often, such an object is a product or service, but it may be information—for example, in the form of a recommended news article.

Despite the many existing algorithms, we can divide recommender systems into several basic approaches. The most common are as follows:

• Summary-based: Non-personal models based on the average product rating
• Content-based: Models based on the intersection of product descriptions and user interests
• Collaborative filtering: Models based on interests of similar user groups
• Matrix factorization: Methods based on the preferences matrix decomposition ...