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Mediation of user models for enhanced personalization in recommender systems

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Abstract

Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia

    Shlomo Berkovsky

  2. University of Haifa, Haifa, Israel

    Shlomo Berkovsky & Tsvi Kuflik

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Francesco Ricci

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  1. Shlomo Berkovsky
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  2. Tsvi Kuflik
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  3. Francesco Ricci
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Correspondence to Shlomo Berkovsky.

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Berkovsky, S., Kuflik, T. & Ricci, F. Mediation of user models for enhanced personalization in recommender systems. User Model User-Adap Inter 18, 245–286 (2008). https://doi.org/10.1007/s11257-007-9042-9

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