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Data Mining Methods for Recommender Systems

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Book cover Recommender Systems Handbook

Abstract

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

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Notes

  1. 1.

    Note that a similarity measure is not a preprocessing step in itself but rather a prerequisite for being able to execute other data mining processes.

  2. 2.

    http://eigentaste.berkeley.edu.

  3. 3.

    http://www.netflixprize.com.

  4. 4.

    http://www.cs.umn.edu/~karypis/metis.

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Authors and Affiliations

  1. Netflix, 100 Winchester Cr., Los Gatos, CA, 95032, USA

    Xavier Amatriain

  2. Quora, 150 Castro St., Mountain View, USA

    Xavier Amatriain

  3. Cliqz, Rosenkavalierplatz 10, 81925, Munich, Germany

    Josep M. Pujol

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Correspondence to Xavier Amatriain .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

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

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Amatriain, X., Pujol, J.M. (2015). Data Mining Methods for Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_7

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