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An algorithmic framework for performing collaborative filtering

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Published:01 August 1999Publication History
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References

  1. 1.ACM. Special issue on information filtering. Communclarions of the ACM, 35(12), December 1992.Google ScholarGoogle Scholar
  2. 2.Marko Balabanovlc and Yoav Shoham. Fab: Contentbased, collaborative recommendation. Communications of the A CM, 40(3):66-72, March 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.Chumki Basu, Haym Hirsh, and William Cohen. Recommendation as classification: using social and contentbased information in recommendation. In Proceedings of the 1998 Workshop on Recommender Systems, pages 11-15. AAAI Press, August 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4.Daniel Billsus and Michael J. Pazzani. Learning collaborative information filters. In Proceedings of the 1998 Workshop on Recommender Systems. AAAI Press, August 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. 5.John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the l~th Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 43- 52, San Francisco, July 24-26 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. 6.Brent J. Dahlen, Joseph A. Konstan, Jon Herlocker, Nathaniel Good, A1 Borchers, and John Riedl. Jumpstarting movielens: User benefits of starting a collaborative filtering system with "dead data". Technical Report TR 98-017, University of Minnesota, 1998.Google ScholarGoogle Scholar
  7. 7.Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K Landauer, and Richard Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391-407, 1990.Google ScholarGoogle ScholarCross RefCross Ref
  8. 8.David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. Using collaborative filtering to weave an information tapestry. Communications of the A CM, 35(12):61-70, December 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.Will Hill, Larry Stead, Mark Rosenstein, and George Furnas. Recommending and evaluating choices in a virtual community of use. In Proceedings of A CM CHI'95 Conference on Human Factors in Computing Systems, pages 194-201, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.Henry Kautz, Bart Selman, and Mehul Shah. Referral Web: Combining social networks and collaborative filtering. Communications of the A CM, 40(3):63-65, March 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the A CM, 40(3):77- 87, March 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.Pattie Maes. Agents that reduce work and information overload. Communications of the A CM, 37(7):30-40, July 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13.David Maltz and Kate Ehrlich. Pointing the way: Active collaborative filtering. In Proceedings of A CM CHI'95 Conference on Human Factors in Computing Systems, pages 202-209, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.James T. McClave and Frank H. Dietrich II. Statistics. Dellen Publishing Company, 1988.Google ScholarGoogle Scholar
  15. 15.Masahiro Morita and Yoichi Shinoda. Information filtering based on user behavior analysis and best match text retrieval. In Proceedings of the Seventeenth Annual International A CM SIGIR Conference on Research and Development in Information Retrieval, pages 272-281, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, New York, NY, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of A CM CSCW'9~ Conference on Computer- Supported Cooperative Work, pages 175-186, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18.Elaine Rich. User modeling via stereotypes. Cognitive Science, 3:335-366, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  19. 19.Gerard Salton and Christopher Buckley. Termweighting approaches in automatic text retreival. Information Processing ~ Management, 24(5):513-523, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.Badrul M. Sarwar, Joseph A. Konstan, A1 Borchers, Jon Herlocker, Brad Miller, and John Riedl. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In Proceedings of 1998 Conference on Computer Supported Collaborative Work, November 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21.Upendra Shardanand and Patti Maes. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of A CM CHI'95 Conference on Human Factors in Computing Systems, pages 210-217, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.John A. Swets. Measuring the accuracy of diagnostic systems. Science, 240(4857):1285-1289, June 1988.Google ScholarGoogle ScholarCross RefCross Ref
  23. 23.Loren Terveen, Will Hill, Brian Amento, David McDonald, and Josh Creter. PHOAKS: A system for sharing recommendations. Communications of the A CM, 40(3):59-62, March 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. 24.C. J. van Rijsbergen. Information Retrieval, chapter 7. Butterworths, 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.NetPerceptions Inc web site. http: //www. net percept ions. com/.Google ScholarGoogle Scholar
  26. 26.James A. Wise, James J. Thomas, Kelly Pennock, David Lantrip, Marc Pottier, Anne Schur, and Vern Crow. Visualizing the non-visual: Spatial analysis and interaction with information from text documents. In IEEE Information Visualization '95, pages 51-58. IEEE Computer Soc. Press, 30-31 October 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
          August 1999
          339 pages
          ISBN:1581130961
          DOI:10.1145/312624

          Copyright © 1999 ACM

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          • Published: 1 August 1999

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          SIGIR '99 Paper Acceptance Rate33of135submissions,24%Overall Acceptance Rate792of3,983submissions,20%

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