Content-based recommender systems aim to recommend items similar to those a user has liked in the past by matching user preferences with item attributes. This chapter provides an overview of these systems, aiming to organize the diverse aspects of their design and implementation. It begins with basic concepts, architecture, and the advantages and drawbacks of content-based recommendations. The second part reviews state-of-the-art systems across various domains, detailing classical and advanced techniques for item and user profile representation. It also covers widely used methods for learning user profiles. The final part discusses trends and future research directions, including the role of user-generated content in adapting to evolving vocabularies and the challenge of providing serendipitous recommendations. The chapter highlights the increasing difficulty of finding relevant information in the digital age, making personalized access crucial. Recommender systems help users navigate large information spaces by tailoring content to their interests. Two main paradigms exist: content-based, which recommends items similar to those a user has liked, and collaborative, which identifies users with similar preferences. This chapter offers a comprehensive study of content-based systems, focusing on current techniques and future trends. It aims to provide an overview of state-of-the-art systems, highlighting effective techniques and their application domains, as well as trends and research directions for the next generation of systems. The chapter is structured into sections covering basic concepts, state-of-the-art systems, trends and future research, and conclusions.Content-based recommender systems aim to recommend items similar to those a user has liked in the past by matching user preferences with item attributes. This chapter provides an overview of these systems, aiming to organize the diverse aspects of their design and implementation. It begins with basic concepts, architecture, and the advantages and drawbacks of content-based recommendations. The second part reviews state-of-the-art systems across various domains, detailing classical and advanced techniques for item and user profile representation. It also covers widely used methods for learning user profiles. The final part discusses trends and future research directions, including the role of user-generated content in adapting to evolving vocabularies and the challenge of providing serendipitous recommendations. The chapter highlights the increasing difficulty of finding relevant information in the digital age, making personalized access crucial. Recommender systems help users navigate large information spaces by tailoring content to their interests. Two main paradigms exist: content-based, which recommends items similar to those a user has liked, and collaborative, which identifies users with similar preferences. This chapter offers a comprehensive study of content-based systems, focusing on current techniques and future trends. It aims to provide an overview of state-of-the-art systems, highlighting effective techniques and their application domains, as well as trends and research directions for the next generation of systems. The chapter is structured into sections covering basic concepts, state-of-the-art systems, trends and future research, and conclusions.