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2018 | OriginalPaper | Buchkapitel

Rating-Based Collaborative Filtering: Algorithms and Evaluation

verfasst von : Daniel Kluver, Michael D. Ekstrand, Joseph A. Konstan

Erschienen in: Social Information Access

Verlag: Springer International Publishing

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Abstract

Recommender systems help users find information by recommending content that a user might not know about, but will hopefully like. Rating-based collaborative filtering recommender systems do this by finding patterns that are consistent across the ratings of other users. These patterns can be used on their own, or in conjunction with other forms of social information access to identify and recommend content that a user might like. This chapter reviews the concepts, algorithms, and means of evaluation that are at the core of collaborative filtering research and practice. While there are many recommendation algorithms, the ones we cover serve as the basis for much of past and present algorithm development. After presenting these algorithms we present examples of two more recent directions in recommendation algorithms: learning-to-rank and ensemble recommendation algorithms. We finish by describing how collaborative filtering algorithms can be evaluated, and listing available resources and datasets to support further experimentation. The goal of this chapter is to provide the basis of knowledge needed for readers to explore more advanced topics in recommendation.

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Titel: Rating-Based Collaborative Filtering: Algorithms and Evaluation
verfasst von: Daniel Kluver
Michael D. Ekstrand
Joseph A. Konstan
Verlag: Springer International Publishing
Buch: Social Information Access
Print ISBN: 978-3-319-90091-9

Electronic ISBN: 978-3-319-90092-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-90092-6_10

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