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
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)
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
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As more information becomes available electronically, tools for finding information of interest to users become increasingly important. Information preferences vary greatly across users, therefore, filtering systems must be highly personalized to serve the individual interests of the user. Our research deals with learning approaches to build user profiles that accurately capture user interests from content (documents) and that could be used for personalized information filtering. The learning mechanisms analyzed in this paper are relevance feedback and a naïve Bayes method. Experiments conducted in the context of a content-based profiling system for movies show the pros and cons of each method.