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GroupLens: an open architecture for collaborative filtering of netnews
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Source Computer Supported Cooperative Work archive
Proceedings of the 1994 ACM conference on Computer supported cooperative work table of contents
Chapel Hill, North Carolina, United States
Pages: 175 - 186  
Year of Publication: 1994
ISBN:0-89791-689-1
Authors
Paul Resnick  MIT Center for Coordination Science, Room E53-325, 50 Memorial Drive, Cambridge, MA
Neophytos Iacovou  University of Minnesota, Department of Computer Science, Minneapolis, Minnesota
Mitesh Suchak  MIT Center for Coordination Science, Room E53-325, 50 Memorial Drive, Cambridge, MA
Peter Bergstrom  University of Minnesota, Department of Computer Science, Minneapolis, Minnesota
John Riedl  University of Minnesota, Department of Computer Science, Minneapolis, Minnesota
Sponsors
SIGGROUP: ACM Special Interest Group on Supporting Group Work
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 73,   Downloads (12 Months): 631,   Citation Count: 364
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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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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.
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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).
 
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Maltz, D.A. Distributing Information for Collaborative Filtering on Usenet Net News. MIT Department of EECS MS Thesis (May, 1994).
 
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Pindyck, R.S. and Rubinfeld, D.L. Econometric Models and Economic Forecasts. MacGraw-Hill, New York, 199 I.
 
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Sheth, B. A Learning Approach to Personalized Information Filtering. MIT Department of EECS MS Thesis (February, 1994).
 
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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).
 
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Suchak, M.A. GoodNews: A Collaborative Filter for Network News. MIT Department of EECS MS Thesis (February, 1994).
 
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CITED BY  367
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Collaborative Colleagues:
Paul Resnick: colleagues
Neophytos Iacovou: colleagues
Mitesh Suchak: colleagues
Peter Bergstrom: colleagues
John Riedl: colleagues

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