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Advances in Collaborative Filtering

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Recommender Systems Handbook

Abstract

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.

This article includes copyrighted materials, which were reproduced with permission of ACM and IEEE. The original articles are:

R. Bell and Y. Koren, “Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights”, IEEE International Conference on Data Mining (ICDM’07), pp. 43–52, © 2007 IEEE. Reprinted by permission.

Y. Koren, “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model”, Proc. 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, © 2008 ACM, Inc. Reprinted by permission. http://doi.acm.org/10.1145/1401890.1401944

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Author information

Authors and Affiliations

  1. Yahoo! Research, Haifa, Israel

    Yehuda Koren

  2. AT&T Labs – Research, Florham Park, NJ, USA

    Robert Bell

Authors
  1. Yehuda Koren
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  2. Robert Bell
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Corresponding author

Correspondence to Yehuda Koren .

Editor information

Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

  3. Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

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Koren, Y., Bell, R. (2011). Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_5

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  • DOI: https://doi.org/10.1007/978-0-387-85820-3_5

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-85819-7

  • Online ISBN: 978-0-387-85820-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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