Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
A vector space model for automatic indexing
Communications of the ACM
Real time user context modeling for information retrieval agents
Proceedings of the tenth international conference on Information and knowledge management
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Aiding knowledge capture by searching for extensions of knowledge models
Proceedings of the 2nd international conference on Knowledge capture
Avaliação comparativa de algoritmos de personalização para direcionamento de conteúdo
CLIHC '05 Proceedings of the 2005 Latin American conference on Human-computer interaction
Journal of Management Information Systems
Seeing is retrieving: building information context from what the user sees
Proceedings of the 13th international conference on Intelligent user interfaces
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Effective information customization systems must adjust their behavior to the user's task context. WordSieve, a text analysis algorithm, generates representations of users' topics of interest based on their browsing patterns. By finding terms associated with sequences of related documents, WordSieve learns topic-relevant keywords in real time with no predetermined corpus. You can use these keywords to form search engine queries to suggest relevant documents to the user. This article sketches the project goals, the WordSieve algorithm, and encouraging experimental results comparing WordSieve to TFIDF (term frequency inverse document frequency) and LSI (latent semantic indexing) at a precision-recall task.