GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Dynamic Models of Expert Groups to Recommend Web Documents
ECDL '01 Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Opinion leader based filtering
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
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Recently many recommender systems have been developed to recommend items in online commerce markets, based on user preferences for a particular user, but they have difficulty in deriving user preferences for users who have not rated many documents. In this paper we use dynamic expert-group models to recommend domain-specific items or documents for unspecified users, while users give feedbacks of relative ratings over the recommended items or documents. In this system, the group members have dynamic authority weights depending on their performance of the ranking evaluations. We have tested two effecttiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable.