Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Personalization on the Net using Web mining: introduction
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
Mediation of user models for enhanced personalization in recommender systems
User Modeling and User-Adapted Interaction
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
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
Improving business rating predictions using graph based features
Proceedings of the 19th international conference on Intelligent User Interfaces
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Nowadays, personalization is considered a powerful approach for designing more precise and easy to use information search and recommendation tools. Since the quality of the personalization provided depends on the accuracy of the user models (UMs) managed by the system, it would be beneficial enriching these models through mediating partial UMs, built by other services. This paper proposes a cross-technique mediation of the UMs from collaborative to content-based services. According to this approach, content-based recommendations are built for the target users having no content-based user model, knowing his collaborative-based user model only. Experimental evaluation conducted in the domain of movies, shows that for small UMs, the personalization provided using the mediated content-based UMs outperforms the personalization provided using the original collaborative UMs.