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Cross-Domain Mediation in Collaborative Filtering

  • Shlomo Berkovsky
  • Tsvi Kuflik
  • Francesco Ricci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users’ ratings stored by collaborative systems operating in different application domains. The paper presents several mediation approaches and initial experimental evaluation demonstrating that the mediation can improve the accuracy of the generated predictions.

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References

  1. 1.
    Berkovsky, S.: Decentralized Mediation of User Models for a Better Personalization. In: Proc. of the AH Conference (2006)Google Scholar
  2. 2.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proc. of the SIGIR Conference (1999)Google Scholar
  3. 3.
    McJones, P.: EachMovie Collaborative Filtering Data Set. HP Research (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Shlomo Berkovsky
    • 1
  • Tsvi Kuflik
    • 1
  • Francesco Ricci
    • 2
  1. 1.University of Haifa, Haifa 
  2. 2.Free University of Bozen-BolzanoItaly

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