This chapter introduces the field of recommender systems. It begins by outlining the core objectives of these systems, tracing the historical development of the field, and covering the major approaches employed. The chapter then focuses on the collaborative filtering (CF) paradigm, detailing both user–user CF methods and matrix factorization techniques. Following this background, we delve into a practical project where we work with the MovieLens dataset to predict user movie ratings. It then goes into the coding and accompanying explanations, where we employ the above two CF approaches toward this prediction task.