Recommender systems are algorithms, programs, and services whose main task is to use data to predict which objects (goods or services) are of interest to a user. There are two main types of recommender systems: content-based and collaborative filtering. Content-based recommender systems are based on data collected from specific products. They recommend objects to a user that are similar to ones the user has previously acquired or shown interest in. Collaborative filtering recommender systems filter out objects that a user might like based on the reaction history of other similar users of these systems. They usually consider the user's previous reactions, too.
In this chapter, we'll look at the implementation of recommender system algorithms based on both content and collaborative filtering. We are going to discuss different approaches for implementing...