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Recommender Systems Handbook pp 39–71Cite as

Data Mining Methods for Recommender Systems

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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.

*Work on the chapter was performed while the author was at Telefonica Research

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Acknowledgments

This chapter has been written with partial support of an ICREA grant from the Generalitat de Catalunya.

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Authors and Affiliations

  1. Telefonica Research, Via Augusta, 122, Barcelona, 08021, Spain

    Xavier Amatriain, Nuria Oliver & Josep M. Pujol

  2. Yahoo! Research, Av.Diagonal, 177, Barcelona, 08018, Spain

    Alejandro Jaimes*

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  1. Xavier Amatriain
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  2. Alejandro Jaimes*
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  3. Nuria Oliver
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  4. 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, 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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Amatriain, X., Jaimes*, A., Oliver, N., Pujol, J.M. (2011). Data Mining Methods for Recommender Systems. 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_2

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