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
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
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Hierarchical classification of HTML documents with WebClassII
ECIR'03 Proceedings of the 25th European conference on IR research
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Word sense disambiguation for exploiting hierarchical thesauri in text classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
User Modeling and User-Adapted Interaction
Learning semantic user profiles from text
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is increasing over time. In this context, the role of user modeling and personalized information access is increasing. This paper focuses on the problem of choosing a representation of documents that can be suitable to induce concept-based user profiles as well as to support a content-based retrieval process. We propose a framework for content-based retrieval, which integrates a word sense disambiguation algorithm based on a semantic similarity measure between concepts (synsets) in the WordNet IS-A hierarchy, with a relevance feedback method to induce semantic user profiles. The document representation adopted in the framework, that we called Bag-Of-Synsets (BOS) extends and slightly improves the classic Bag-Of-Words (BOW) approach, as shown by an extensive experimental session.