Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
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Algorithms designed to support users in retrieving relevant information base their relevance computations on user profiles, in which representations of the users interests are maintained. This paper focuses on the use of supervised machine learning techniques to induce user profiles for Intelligent Information Access. The access must be personalized by profiles allowing users to retrieve information on the basis of conceptual content. To address this issue, we propose a method to learn sense-based user profiles based on WordNet, a lexical database.