A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
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
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Information Retrieval
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
Machine learning in automated text categorisation
Machine learning in automated text categorisation
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Dynamic Expert Group Models for Recommender Systems
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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Recently most recommender systems have been developed to recommend items or documents 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 groups which are automatically formed to recommend domain-specific documents for unspecified users. The group members have dynamic authority weights depending on their performance of the ranking evaluations. Human evaluations over web pages are very effective to find relevant information in a specific domain. In addition, we have tested several effectiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable. We show simulation results to check the possibility of dynamic expert group models for recommender systems.