Agents that reduce work and information overload
Communications of the ACM
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web
User Modeling and User-Adapted Interaction
Ubiquitous User Modeling for Situated Interaction
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Tailoring Privacy to Users' Needs
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
User identification for cross-system personalisation
Information Sciences: an International Journal
Identifying Inter-Domain Similarities through Content-Based Analysis of Hierarchical Web-Directories
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Integrating web service and semantic dialogue model for user models interoperability on the web
Journal of Intelligent Information Systems
User model interoperability: a survey
User Modeling and User-Adapted Interaction
Decentralized mediation of user models for a better personalization
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Entertainment personalization mechanism through cross-domain user modeling
INTETAIN'05 Proceedings of the First international conference on Intelligent Technologies for Interactive Entertainment
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
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The existing personalization services usually base on proprietary and partial user models. This work attempts at evolving inference-based mediation mechanism that will facilitate integrating user models coming from different sources, such as repositories of other service providers and user's personal devices. This will allow obtaining more information about the users and providing more accurate personalization. The efficiency of the above approach will be demonstrated using the techniques from Recommender Systems domain.