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Computing Recommendations with Collaborative Filtering

Computing Recommendations with Collaborative Filtering

Neal Lathia
Copyright: © 2009 |Pages: 19
ISBN13: 9781605663067|ISBN10: 1605663069|ISBN13 Softcover: 9781616924829|EISBN13: 9781605663074
DOI: 10.4018/978-1-60566-306-7.ch002
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MLA

Lathia, Neal. "Computing Recommendations with Collaborative Filtering." Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, et al., IGI Global, 2009, pp. 23-41. https://doi.org/10.4018/978-1-60566-306-7.ch002

APA

Lathia, N. (2009). Computing Recommendations with Collaborative Filtering. In M. Chevalier, C. Julien, & C. Soule-Dupuy (Eds.), Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling (pp. 23-41). IGI Global. https://doi.org/10.4018/978-1-60566-306-7.ch002

Chicago

Lathia, Neal. "Computing Recommendations with Collaborative Filtering." In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, Christine Julien, and Chantal Soule-Dupuy, 23-41. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-306-7.ch002

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Abstract

Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.

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