GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Experience with personalization of Yahoo!
Communications of the ACM
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Web montage: a dynamic personalized start page
Proceedings of the 11th international conference on World Wide Web
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Out of context: computer systems that adapt to, and learn from, context
IBM Systems Journal
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
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Using a browsing context is one of the keys to web site access personalization under particular constraints. With poor user information modeling, which is a common situation, a web site cannot be adapted to the current user. Assuming the current clickstream is the only known information about a web site user (no profile, no past sessions, no identification, no content analysis of viewed pages), we propose here a method to enrich the browsing context and enhance the current user model. In a batch mode, profile-based enriched navigation patterns are computed. In on-line mode, Navire, a personal agent and its matching rule engine continually re-adapts the browsing context with pre-calculated profiles. Based on the current up-to-date context, Navire personalizes the access to a web site.