Overview of Recommender Systems

This chapter first introduces the history of the recommender system and the revolutionary changes in the field of recommender systems. Then, this chapter introduces the basic principles of recommender systems, including introducing the basic assumptions of recommendation algorithms from the perspective of machine learning, introducing how to define the recommendation problem in the form of a machine learning problem, and emphatically introducing the deep learning-based paradigm to solve the recommendation problem—“representation learning + interaction function learning”. This chapter also gives an overview of the technical architecture of recommender systems, including the differences between small- and medium-scale recommender systems and large-scale recommender systems. Finally, this chapter introduces the main application areas of recommender systems, such as e-commerce, content platforms, etc., and the actual business value brought by recommender systems to these application areas and compares the three main applications in the Internet field—search, advertising, and recommendation, by the differences and connections among them. Starting from industry problems, this chapter summarizes the differences in the application of recommender systems in different industries and outlines the solutions to different types of problems.