Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Information retrieval
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Agents that reduce work and information overload
Communications of the ACM
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PHOAKS: a system for sharing recommendations
Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
KQML as an agent communication language
Software agents
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Proceedings of the HCI International '99 (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Communication, Cooperation, and Application Design-Volume 2 - Volume 2
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Collaborative Filtering systems suggest items to a user because it is highly rated by some other user with similar tastes. Although these systems are achieving great success on web based applications, the tremendous growth in the number of people using these applications require performing many recommendations per second for millions of users. Technologies are needed that can rapidly produce high quality recommendations for large community of users.In this paper we present an agent based approach to collaborative filtering where agents work on behalf of their users to form shared "interest groups", which is a process of pre-clustering users based on their interest profiles. These groups are dynamically updated to reflect the user's evolving interests over time. We further present a multi-agent based simulation of the architecture as a means of evaluating the system.