Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
User interactions with everyday applications as context for just-in-time information access
Proceedings of the 5th international conference on Intelligent user interfaces
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
OIL: An Ontology Infrastructure for the Semantic Web
IEEE Intelligent Systems
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Information retrieval in context: IRiX
ACM SIGIR Forum
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Semantic representation of context models: a framework for analyzing and understanding
Proceedings of the 1st Workshop on Context, Information and Ontologies
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The key for providing a robust context for personalized information retrieval is to build a library which gathers the long term and the short term user's interests and then using it in the retrieval process in order to deliver results that better meet the user's information needs. In this paper, we present an enhanced approach for learning a semantic representation of the underlying user's interests using the search history and a predefined ontology. The basic idea is to learn the user's interests by collecting evidence from his search history and represent them conceptually using the concept hierarchy of the ontology. We also involve a dynamic method which tracks changes of the short term user's interests using a correlation metric measure in order to learn and maintain the user's interests.