This chapter provides a comprehensive overview of content-based recommender systems, aiming to organize the diverse aspects involved in their design and implementation. The first part introduces the basic concepts, terminology, and a high-level architecture of content-based recommenders, along with their main advantages and drawbacks. The second part reviews the state of the art in various application domains, detailing both classical and advanced techniques for representing items and user profiles. It also discusses the most widely used methods for learning user profiles. The final part explores future trends and research directions, including the role of User-Generated Content (UGC) in evolving vocabularies and the challenge of providing serendipitous recommendations. The chapter is structured to highlight effective techniques, application domains, and potential advancements in content-based recommender systems.This chapter provides a comprehensive overview of content-based recommender systems, aiming to organize the diverse aspects involved in their design and implementation. The first part introduces the basic concepts, terminology, and a high-level architecture of content-based recommenders, along with their main advantages and drawbacks. The second part reviews the state of the art in various application domains, detailing both classical and advanced techniques for representing items and user profiles. It also discusses the most widely used methods for learning user profiles. The final part explores future trends and research directions, including the role of User-Generated Content (UGC) in evolving vocabularies and the challenge of providing serendipitous recommendations. The chapter is structured to highlight effective techniques, application domains, and potential advancements in content-based recommender systems.