Learning User Preferences for 2CP-Regression for a Recommender System

  • Alan Eckhardt
  • Peter Vojtáš
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5901)


In this paper we deal with a task to learn a general user model from user ratings of a small set of objects. This general model is used to recommend top-k objects to the user. We consider several (also some new) alternatives of learning local preferences and several alternatives of aggregation (with or without 2CP-regression). The main contributions are evaluation of experiments on our prototype tool PrefWork with respect to several satisfaction measures and the proposal of method Peak for normalisation of numerical attributes. Our main objective is to keep the number of sample data which the user has to rate reasonable small.


Recommender System User Model User Preference Local Preference Aggregation Function 
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 2010

Authors and Affiliations

  • Alan Eckhardt
    • 1
    • 2
  • Peter Vojtáš
    • 1
    • 2
  1. 1.Department of Software EngineeringCharles University 
  2. 2.Institute of Computer ScienceCzech Academy of SciencePragueCzech Republic

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