Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Fab: content-based, collaborative recommendation
Communications of the ACM
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
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Learning user profiles from text in e-commerce
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
WordNet-Based word sense disambiguation for learning user profiles
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Improving Social Filtering Techniques Through WordNet-Based User Profiles
UM '07 Proceedings of the 11th international conference on User Modeling
Automated feature generation from structured knowledge
Proceedings of the 20th ACM international conference on Information and knowledge management
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This paper focuses on the problem of choosing a representation of documents that can be suitable to induce more advanced semantic user profiles, in which concepts are used instead of keywords to represent user interests. We propose a method which integrates a word sense disambiguation algorithm based on the WordNet IS-A hierarchy, with two machine learning techniques to induce semantic user profiles, namely a relevance feedback method and a probabilistic one. The document representation proposed, that we called Bag-Of-Synsets improves the classic Bag-Of-Words approach, as shown by an extensive experimental session.