These types of recommender systems are employed in specific domains where the purchase history of the users is smaller. In such systems, the algorithm takes into consideration the knowledge about the items, such as features, user preferences asked explicitly, and recommendation criteria, before giving recommendations. The accuracy of the model is judged based on how useful the recommended item is to the user. Take, for example, a scenario in which you are building a recommender system that recommends household electronics, such as air conditioners, where most of the users will be first timers. In this case, the system considers features of the items, and user profiles are generated by obtaining additional information from the users, such as specifications, and then recommendations are made. These types of system are called constraint-based recommender systems, which we will learn more about in subsequent chapters.

Before building these types of recommender systems, we take into consideration the following questions: