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.
Bibliographical noteFunding Information:
Acknowledgements The authors gratefully acknowledge the support of the Caesarea Edmond Benjamin de Rothschild Foundation Institute for Interdisciplinary Applications of Computer Science (CRI) in Haifa, Israel, and of the Istituto Trentino di Cultura—the Center for Scientific and Technological Research (ITC-irst) in Trento, Italy.
- Mediation of user modeling data
- Recommender systems
- Ubiquitous user modeling
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Science Applications