Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Capturing knowledge of user preferences: ontologies in recommender systems
Proceedings of the 1st international conference on Knowledge capture
Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
User Modeling and User-Adapted Interaction
Meta-searches in peer-to-peer networks
Personal and Ubiquitous Computing
Collecting community wisdom: integrating social search & social navigation
Proceedings of the 12th international conference on Intelligent user interfaces
ASSIST: adaptive social support for information space traversal
Proceedings of the eighteenth conference on Hypertext and hypermedia
Recommending topics for self-descriptions in online user profiles
Proceedings of the 2008 ACM conference on Recommender systems
Deduced social networks for an educational digital library
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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The size and diversity of the Web has been the root cause of the poor performance of many retrieval systems, with little navigational support provided by many large online formation repositories. The online information retrieval process cross different repositories shares similarities with content access facilities and user behaviors even when containing inherently different content types. In this work, we introduce our social recommender system called ASSIST. The recommendation framework in ASSIST can be applied to any online information retrieval service with key information access components, search and browsing. ASSIST exploits multiple forms of social implicit feedback in order to generate well-informed user recommendations in the online information retrieval domain.