Chapter 2 Data Mining Methods for Recommender Systems

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 prepro-cessing 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 effi-cient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied. Recommender Systems (RS) typically apply techniques and methodologies from other neighboring areas – such as Human Computer Interaction (HCI) or Informa-tion Retrieval (IR). However, most of these systems bear in their core an algorithm that can be understood as a particular instance of a Data Mining (DM) technique