Multi-agent learning in recommender systems for information filtering on the internet

International Journal of Cooperative Information Systems

Volumn 10, Issue 1-2, 2001, Pages 81-100

  Delgado, Joaquin ^a   Ishii, Naohiro ^b  

^a TripleHop Technologies Inc   (United States)

^b NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Information filtering; MAS; Online learning; Recommender systems

Indexed keywords

EID: 0035579375     PISSN: 02188430     EISSN: None     Source Type: Journal    
DOI: 10.1142/s0218843001000266     Document Type: Article

References (19)

1. J. Delgado, N. Ishii and T. Ura, Content-based collaborative information filtering: Actively learning to classify and recommend documents, eds. M. Klush, G. Weiss, Cooperative Agents II, Proc./CIA'98, Lecture Nutes in Artificial Intelligence Series 1435 (Springer-Verlag, 1998) 206-215.
2. M. Balabanovic and Y. Shoham, Fab: Content-based, collaborative recommendation, Comm. A CM 40, 3 (March 1997) 66-70.
3. C. Basu, H. Hirsh and W. Cohen, Recommendation as classification: Using social and content-based information in recommendation, Proc. AAAI-98, Madison, WI, USA (July 1998) 714-726.
4. J. Breese, D. Heckerman and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proc. 14th Conf. Uncertainty Artif. Intell., Madison, WI, 1998.
5. P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom and J. Riedl, GroupLens: An open architecture for collaborative filtering of netnews, Proc. CSCW 1994 Conf., 1994.
6. U. Shardanand and P. Maes, Social information filtering: Algorithms for automation of "Word of Mouth", Proc. ACM/CHI'95, 1995.
7. H. Varian and P. Resnick, Recommender systems, Special section in Comm. ACM 40, 3 (March 1997).
8. G. Salton and C. Buckley, Improving retrieval performance by relevance feedback, J. American Society Inf. Sci. 41 (1990) 288-297.
9. N. Littlestone and M. Warmuth, The Weighted-majority algorithm, Inf. Comput. 108, 2 (1994) 212-261.
10. N. Littlestone, Learning quickly when irrelevant attributes abound a new linear-threshold algorithm, Machine Learning 2 (1988) 285-318.
11. A. Blum, Online algorithms in machine learning (a survey), This is a survey paper for a talk given at the Dagstuhl workshop on online algorithms, 1996.
12. A. Blum, Empirical support for Winnow and weighted-majority based algorithms: Results on a calendar-scheduling domain, Machine Learning 26 (1997) 5-23.
13. S. Goldman and M. Warmuth, Learning binary relation using weighted-majority voting, Machine Learning 20 (1995) 245-271.
14. K. Goldberg, D. Gupta, M. Digiovanni and H. Narita, Jester 2.0: Evaluation of a new linear tune collaborative filtering algorithm, Poster Session and Demonstration, 22nd Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, August 1999.
15. D. Billsus and M. Pazzani, Learning collaborative filters, Proc. ICML'98, Madison, WI, USA (Morgan Kaufman, 1998) 46-53.
16. J. Delgado and N. Ishii, Content + Collaboration = Recommendation, Papers from the AAAI 1998 Workshop on Recommender Systems, Technical Report WS-98-08 (AAAI Press, 1998) 37-41.
17. L. Ungar and D. Foster, Clustering methods for collaborative filtering, Papers from the AAAI 1998 Workshop on Recommender System, Technical Report WS-98-08 (AAAI Press, 1998).
18. A. Nakamura and N. Abe, Collaborative filtering using weighted-majority prediction algorithms, Proc. ICML'98 (Morgan Kaufman, 1998) 395-403.
19. P. McJones, EachMovie collaborative filtering data set, DEC Systems Research Center, http://www.research/digital.com/SRC/eachmovie, 1997.