Recommender Systems

  • Panagiotis Symeonidis
  • Dimitrios Ntempos
  • Yannis Manolopoulos
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

Recommender systems base their operation on past user purchases/ratings over a collection of items, for instance, books, CDs, etc. Collaborative Filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this chapter, we bring to surface factors that affect recommendation process. Moreover, we describe the most important problems related to recommender systems and give some references to actual solutions. Finally, there is an economic and social report regarding recommender systems, which examines them under a rather market-based angle.

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Panagiotis Symeonidis
    • 1
  • Dimitrios Ntempos
    • 2
  • Yannis Manolopoulos
    • 3
  1. 1.Department of Informatics Data Engineering LaboratoryAristotle University of ThessalonikiStavroupoli, ThessalonikiGreece
  2. 2.Kiwe DevelopmentThessalonikiGreece
  3. 3.Department of Informatics Data Engineering LabAristotle University of ThessalonikiStavroupoli, ThessalonikiGreece

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