A multi-agent architecture for dynamic collaborative filtering
Gulden Uchyigit and Keith L. Clark
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.
In Proceedings of the 5th International Conference on Enterprise Information Systems, Angers, France, April 22-26, 2003 IEEE, AAI, ACM
for a compressed PostScript version of the paper (iceis03.pdf.gz, 82368 bytes).