Recommendation systems are a common feature in modern applications, suggesting products, movies, or articles to users. Before constructing these systems, it is necessary to understand their core components and the data they operate on.

This chapter introduces the main types of recommendation algorithms you will encounter, such as collaborative filtering and content-based methods. We will examine the structure of user-item interaction data, which forms the basis for these algorithms, and differentiate between explicit and implicit forms of user feedback. You will also learn about a common operational issue known as the cold-start problem. The chapter concludes with practical steps for setting up your development environment and loading a dataset, preparing you for the hands-on work in the sections that follow.