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GroupLens: an open architecture for collaborative filtering of netnews

Authors:

Neophytos Iacovou,

Peter Bergstrom, and

John RiedlAuthors Info & Claims

CSCW '94: Proceedings of the 1994 ACM conference on Computer supported cooperative work

October 1994

Pages 175 - 186

https://doi.org/10.1145/192844.192905

Published: 22 October 1994 Publication History

Abstract

Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

References

[1]

Allen, ll.B. User MoOels: ltleory, Memoo, ana Practice. International Journal of Man-Machine Studies, 32, (1990), pp. 511-543.

Digital Library

[2]

Belkin, N.J. and Croft, B.W. Information Filtering and Information Retrieval" Two Sides of the Same Coin? CA CM, 35, 12 (1992), pp. 29-38.

Digital Library

[3]

Brothers, L., Hollan, J., Nielsen, J., Stornetta, S., Abney, S., Furnas, G. and Littman, M. Supporting Informal Communication via Ephemeral Interest Groups. In Proceedings of CSCW 92 (1992, New York: ACM), pp. 84-90.

Digital Library

[4]

Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41, 6 (1990), pp. 391-407.

[5]

Foltz, P.W. and Dumais, S.T. Personalized Information Delivery: An Analysis of Information Filtering Methods. Communications of the ACM, 35, 12 (1992), pp. 51-60.

Digital Library

[6]

Goldberg, D., Nichols, D., Old, B.M. and Terry. D. Using Collaborative Filtering to Weave an Information Tapestry. Communications of the A CM, 35, 12 (1992), pp. 61-70.

Digital Library

[7]

Hill, W.C., Hollan, J.D., Wroblewski, D. and McCandless, T. Edit Wear and Read Wear. in Proceedings of CH192 Conference on Human Factors in Computing Systems (1992, New York: ACM), pp. 3-9.

Digital Library

[8]

Kahn, R.E. and Cerf, V.G. The Digital Library Project, Volume i: The Wold of Knowbots. An Open Architecture for a Digital Library System and a Plan for Its Development. CNRI, 1895 Preston White Drive, Suite 100, Reston, VA 22091 Tech Report (March, 1988).

[9]

Karlgren, J. Newsgroup Clustering Based on User Behavior-- A Recommendation Algebra. Swedish Institute of Computer Science #SICS-T--94/04-SE (March, 1994).

Digital Library

[10]

Kawell, L.J., Beckhardt, S., Halvorsen, T. and Ozzie, R. Replicated Document Management in a Group Communication System. In Proceedings of CSCW 88 (1988, New York: ACM).

Digital Library

[11]

Kumon, S. From Wealth to Wisdom: A Change in the Social Paradigm. In Proceedings of CSCW 92 (1992, New York: ACM), pp. 3.

Digital Library

[12]

Maes, P. and Kozierok, R. Learning Interface Agents. in Proceedings of AAAI 93 (1993, San Mateo, CA: American Association for Artifical Intelligence).

[13]

Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D. intelligent Information Sharing Systems. Communications of the A CM, 30, 5 (1987), pp. 390-402.

Digital Library

[14]

Maltz, D.A. Distributing Information for Collaborative Filtering on Usenet Net News. MIT Department of EECS MS Thesis (May, 1994).

Digital Library

[15]

Pindyck, R.S. and Rubinfeld, D.L. Econometric Models and Economic Forecasts. MacGraw-Hill, New York, 199 I.

[16]

Salton, G. and Buckley, C. Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management, 24, 5 (1988), pp. 513-523.

Digital Library

[17]

Salton, G. and Buckley, C. Improving Retrieval Performance by Relevance Feedback. Journal of the American Society for Information Science, 41, 4 (1990), pp. 288-297.

[18]

Sheth, B. A Learning Approach to Personalized Information Filtering. MIT Department of EECS MS Thesis (February, 1994).

[19]

Stodolsky, D.S. Invitational Journals Based Upon Peer Consensus. Roskilde University Centre, Institute of Geography, Socioeconomic Analysis, and Computer Science. ISSN 0109-9779-29 #No. 29/1990 (, 1990).

[20]

Suchak, M.A. GoodNews: A Collaborative Filter for Network News. MIT Department of EECS MS Thesis (February, 1994).

[21]

Wiederhold, G. Mediators in the Architecture of Future Information Systems. IEEE Computer, March, (1992), pp. 38-49.

Digital Library

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Index Terms

GroupLens: an open architecture for collaborative filtering of netnews
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

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cover image ACM Conferences

CSCW '94: Proceedings of the 1994 ACM conference on Computer supported cooperative work

October 1994

464 pages

ISBN:0897916891

DOI:10.1145/192844

Chairmen:
John B. Smith
Univ. of North Carolina
,
F. Don Smith
IBM Corporation
,
Thomas W. Malone
Massachusetts Institute of Technology

Copyright © 1994 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 October 1994

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October 22 - 26, 1994

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CSCW '94 Paper Acceptance Rate 42 of 200 submissions, 21%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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