Data Mining Methods for Recommender Systems

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

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.

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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
  1. Xavier Amatriain