Content-based recommendation systems recommend items to users based on item descriptions and user profiles. These systems are used in various domains, including web pages, news articles, restaurants, and products. They typically involve describing items, creating user profiles based on user preferences, and comparing items to the profile to determine recommendations. User profiles are often automatically created and updated based on user feedback. A common scenario for recommendation systems is a web application where users interact with a list of items. The system presents a summary list, and the user selects an item for more details or interaction. For example, online news sites present headlines and allow users to read stories, while e-commerce sites display product lists and allow purchases. Web servers often use databases to store items and dynamically generate web pages. This chapter discusses alternative item representations, recommendation algorithms for each, and variations of these approaches. It also covers the strengths and weaknesses of content-based systems and future research directions. Items are often stored in a database with attributes such as cuisine or service. A simple restaurant database is provided as an example of structured data. However, unstructured data, such as text descriptions or reviews, can complicate profile creation. For instance, a user's preference for French restaurants might be inferred from positive reviews. News articles are an example of unstructured data, where the entire article can be treated as a large text field.Content-based recommendation systems recommend items to users based on item descriptions and user profiles. These systems are used in various domains, including web pages, news articles, restaurants, and products. They typically involve describing items, creating user profiles based on user preferences, and comparing items to the profile to determine recommendations. User profiles are often automatically created and updated based on user feedback. A common scenario for recommendation systems is a web application where users interact with a list of items. The system presents a summary list, and the user selects an item for more details or interaction. For example, online news sites present headlines and allow users to read stories, while e-commerce sites display product lists and allow purchases. Web servers often use databases to store items and dynamically generate web pages. This chapter discusses alternative item representations, recommendation algorithms for each, and variations of these approaches. It also covers the strengths and weaknesses of content-based systems and future research directions. Items are often stored in a database with attributes such as cuisine or service. A simple restaurant database is provided as an example of structured data. However, unstructured data, such as text descriptions or reviews, can complicate profile creation. For instance, a user's preference for French restaurants might be inferred from positive reviews. News articles are an example of unstructured data, where the entire article can be treated as a large text field.