Abstract
A prerequisite for implementing collaborative filtering recommender systems is the availability of users’ preferences data. This data, typically in the form of ratings, is exploited to learn the tastes of the users and to serve them with personalized recommendations. However, there may be alack of preference data, especially at the initial stage of the operations of a recommender system, i.e., in the Cold Start phase. In particular, when a new user has not yet rated any item, the system would be incapable of generating relevant recommendations for this user. Or, when a new item is added to the system catalogue and no user has rated it, the system cannot recommend this item to any user.This chapter discusses the cold start problem and provides a comprehensive description of techniques that have been proposed to address this problem. It surveys algorithmic solutions and provides a summary of the performance comparison. Moreover, it lists publicly available resources(e.g., libraries and datasets) and offers a set of practical guidelines that can be adopted by researchers and practitioners