Abstract
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 refiect 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.
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Uchyigit, G., Clark, K. (2002). Agents That Model and Learn User Interests for Dynamic Collaborative Filtering. In: Klusch, M., Ossowski, S., Shehory, O. (eds) Cooperative Information Agents VI. CIA 2002. Lecture Notes in Computer Science(), vol 2446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45741-0_14
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DOI: https://doi.org/10.1007/3-540-45741-0_14
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