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State-of-the-Art Recommender Systems

State-of-the-Art Recommender Systems

Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
Copyright: © 2009 |Pages: 22
ISBN13: 9781605663067|ISBN10: 1605663069|ISBN13 Softcover: 9781616924829|EISBN13: 9781605663074
DOI: 10.4018/978-1-60566-306-7.ch001
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MLA

Candillier, Laurent, et al. "State-of-the-Art Recommender Systems." Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, et al., IGI Global, 2009, pp. 1-22. https://doi.org/10.4018/978-1-60566-306-7.ch001

APA

Candillier, L., Jack, K., Fessant, F., & Meyer, F. (2009). State-of-the-Art Recommender Systems. In M. Chevalier, C. Julien, & C. Soule-Dupuy (Eds.), 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

Chicago

Candillier, Laurent, et al. "State-of-the-Art Recommender Systems." In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, Christine Julien, and Chantal Soule-Dupuy, 1-22. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-306-7.ch001

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Abstract

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.

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