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Rating-Based Collaborative Filtering: Algorithms and Evaluation

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Social Information Access

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

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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Notes

  1. 1.

    https://www.pandora.com/.

  2. 2.

    http://eigentaste.berkeley.edu/.

  3. 3.

    https://movielens.org/.

  4. 4.

    https://www.amazon.com/.

  5. 5.

    http://www.imdb.com/.

  6. 6.

    https://lenskit.org/.

  7. 7.

    https://mahout.apache.org/.

  8. 8.

    http://mymedialite.net/.

  9. 9.

    available at http://grouplens.org/datasets/movielens/.

  10. 10.

    available (with updates) at http://eigentaste.berkeley.edu/dataset/.

  11. 11.

    available at http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

  12. 12.

    available at http://jmcauley.ucsd.edu/data/amazon/.

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Authors and Affiliations

  1. GroupLens Research, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

    Daniel Kluver & Joseph A. Konstan

  2. People and Information Research Team (PIReT), Department of Computer Science, Boise State University, Boise, USA

    Michael D. Ekstrand

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    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

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Kluver, D., Ekstrand, M.D., Konstan, J.A. (2018). Rating-Based Collaborative Filtering: Algorithms and Evaluation. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_10

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