Mediation of user models for enhanced personalization in recommender systems

Abstract. Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This paper 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 paper 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 crossrepresentation. Finally, the paper 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...

• Type: Journal
• Year: 2008
• Where: UMUAI
• Authors: Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci
• Keywords: Mediation, Recommender Systems, UMUAI 2008, User