A Case-Based Reasoning Approach
to Collaborative Filtering

Robin Burke

Department of Information and Computer Science
University of California, Irvine
burke@ics.uci.edu


Abstract. Collaborative filtering systems make recommendations based
on the accumulation of ratings by many users. The process has a case-based 
reasoning flavor: recommendations are generated by looking at
the behavior of other users who are considered similar. However, the
features associated with a user are semantically weak compared with
those used by CBR systems. This research examines multi-dimensional
or semantic ratings in which a system gets information about the reason
behind a preference. Experiments show that metrics in which the
semantic meaning of each rating is taken into account have markedly
superior performance than simpler techniques.
References

[1]	Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedi, 1. 1994
GroupLens: an open architecture for collaborative filtering of netnews. In CSCW
94: Proceedings of the conference on Computer supported cooperative work,
pp. 175-186. New York: ACM Press.
[2]	Shardanand, U. and Maes, P. 1995. Social information filtering algorithms for
automating word of mouth In CHI-95: Conference proceedings on Human
factors in computing systems, pp. 210-217. New York: ACM Press.
[3]	Resnick, P. and Varian, H. R. 1997. Recommender systems. Communications of
the ACM, 40(3) 56-58.
[4]	Kautz, H. (ed.) 1998 Recommender Systems: Papers from the AAAI Workshop.
AAAI Technical Report WS-98-08. AAAI.
[5]	Balabanovic, M. and Shoham, Y. 1997. FAB: Content-Based Collaborative
Recommender. . Communications of the ACM, 40(3) 6 6-72.
[6]	Pazzani, M. and Bilisus, D. 1997. Learning and revising user profiles: The
identification of interesting web sites. Machine Learning 27(3): 3 13-331.
[7]	Burke, R. 1999 The Wasabi Personal Shopper: A Case-Based Recommender
System. In Proceedings of the 11th National Conference on Innovative
Applications of Artificial Intelligence, pp. 844-849, AAAI.
[8]	Burke, R. In press. Knowledge-based Recommender Systems. In A. Kent (ed.),
Encyclopedia ofLibrary and Information Systems.
[9]	Burke, R., Hammond, K., and Young, B. 1997 The FindMe Approach to
Assisted Browsing. IEEE Expert, 12(4), 32-40.
[10]	Burke, R. 1999 Integrating Knowledge-Based and Collaborative-Filtering
Recommender Systems. In AAAI Workshop on Al in Electronic Commerce. pp.
69-72. AAAI.
[11]	Cotter, P. & Smyth, B. 2000. PTV: Intelligent Personalised TV Guides. In
Proceedings of the 12th Innovative Applications of Artificial Intelligence
Conference. AAAI Press. To appear.
[12]	Billsus, D. and Pazzani, M. 1999. A Hybrid User Model for News Story
Classification. In Proceedings of the Seventh International Conference on User
Modeling (UM 99), Banff, Canada, June 20-24, 1999.
[13]	Breese, J. S.; Heckerman, D. and Kadie, C. 1998. Analysis of Predictive
Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference
on Uncertainty in Artificial lntelligence, pp. 43-52. San Francisco, CA: Morgan
Kaufmann.
