An instructable, adaptive interface for discovering and monitoring information on the World-Wide Web
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Architecture of a metasearch engine that supports user information needs
Proceedings of the eighth international conference on Information and knowledge management
User interactions with everyday applications as context for just-in-time information access
Proceedings of the 5th international conference on Intelligent user interfaces
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Constructing Web User Profiles: A non-invasive Learning Approach
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Vers la définition du contexte d'un utilisateur mobile de système de recherche d'information
Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
Custom ordering on digital library information retrieval
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
A Multi-agent System Using Ontological User Profiles for Dynamic User Modelling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Study on user preferences modelling based on web mining
International Journal of Information Technology and Management
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Search engines, generally, return results without any regard for the concepts in which the user is interested. In this paper, we present our approach to personalizing search engines using ontology based contextual profiles. In contrast to long-term user profiles, we construct contextual user profiles that capture what the user is working on at the time they conduct a search. These profiles are used to personalize the search results to suit the information needs of the user at a particular instant of time. We present the results of experiments evaluating the effect of the original versus conceptual ranking and the use of multiple sources of information to build the contextual profile. We were able to achieve a 15% improvement over Google in the average rank of the result clicked by a user when contextual information extracted from open Word documents and Web pages was used to re-rank the results.