Recommender Systems

Recommender systems have been studied in the context of a range of domains, including information retrieval, the Internet, e‐commerce, Web usage mining, and many others. The key problem addressed by recommendation may be summarized as an estimation of scores for items that have not yet been seen by a user. Recommender systems may be classified according to three approaches: score estimation method, the data used to estimate scores or the main objective of the system. Whatever recommendation technique is used, certain information needs to be considered in relation to users; this information is stored in user profiles. Recommender systems traditionally make use of techniques that are based on data mining techniques. The chapter also discusses content‐based, collaborative filtering, knowledge‐based, and hybrid recommender systems, and some other types of approaches.