A Preference-Based Recommender System

  • Benjamin Satzger
  • Markus Endres
  • Werner Kießling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


The installation of recommender systems in e-applications like online shops is common practice to offer alternative or cross-selling products to their customers. Usually collaborative filtering methods, like e.g. the Pearson correlation coefficient algorithm, are used to detect customers with a similar taste concerning some items. These customers serve as recommenders for other users. In this paper we introduce a novel approach for a recommender system that is based on user preferences, which may be mined from log data in a database system. Our notion of user preferences adopts a very powerful preference model from database systems. An evaluation of our prototype system suggests that our prediction quality can compete with the widely-used Pearson-based approach. In addition, our approach can achieve an added value, because it yields better results when there are only a few recommenders available. As a unique feature, preference-based recommender systems can deal with multi-attribute recommendations.


Markov Chain Monte Carlo Recommender System User Preference Linear Extension Preference Order 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Benjamin Satzger
    • 1
  • Markus Endres
    • 1
  • Werner Kießling
    • 1
  1. 1.Institute of Computer ScienceUniversity of AugsburgAugsburgGermany

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