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 collaborative filtering algorithms, implement systems using only the linear algebra library, and see how to use the mlpack library to solve collaborative filtering problems. We'll use the MovieLens dataset provided by GroupLens from a research lab in the Department of Computer Science and Engineering at the University of Minnesota: https://grouplens.org/datasets/movielens/

The following topics will be covered in this chapter:

• An overview of recommender system algorithms
• Understanding collaborative filtering method details
• Examples of item-based collaborative filtering with C++