Mediation of user models for enhanced personalization in recommender systems

Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci

Research output: Contribution to journalArticlepeer-review

141 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)245-286
Number of pages42
JournalUser Modeling and User-Adapted Interaction
Volume18
Issue number3
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Recommender systems
  • ubiquitous user modeling
  • mediation of user modeling data

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