Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45741-0_14 | CIA |
Keywords | Field | DocType |
great success,collaborative filtering system,interest profile,tremendous growth,large community,similar taste,interest group,high quality recommendation,pre-clustering user,dynamic collaborative filtering,learn user interests,web based applications,collaborative filtering | World Wide Web,Architecture,User assistance,Collaborative filtering,Computer science,Filter (signal processing),User modeling,Web application,User interface,Distributed computing,The Internet | Conference |
ISBN | Citations | PageRank |
3-540-44173-5 | 0 | 0.34 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gulden Uchyigit | 1 | 5 | 5.53 |
| Keith L. Clark | 2 | 275 | 64.57 |