Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
User Modeling and User-Adapted Interaction
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An intelligent personalized service for conference participants
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity. We developed a semantic recommender system, called ITem Recommender, able to disambiguate documents before using them to learn the user profile. The Conference Participant Advisor service relies on the profiles learned by ITem Recommender to build a personalized conference program, in which relevant talks are highlighted according to the participant's interests.