The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users. © 2009, IGI Global.
CITATION STYLE
Candillier, L., Jack, K., Fessant, F., & Meyer, F. (2009). State-of-the-art recommender systems. In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling (pp. 1–22). IGI Global. https://doi.org/10.4018/978-1-60566-306-7.ch001
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