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.


Bayesian Network Singular Value Decomposition Association Rule Recommender System Latent Dirichlet Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.NetflixLos GatosUSA
  2. 2.QuoraMountain ViewUSA
  3. 3.CliqzMunichGermany

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