Collaborative filtering and the generalized vector space model (poster session)

ABSTRACT

Collaborative filtering is a technique for recommending documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector space model of information retrieval represents a document by a vector of its similarities to all other documents. The process of collaborative filtering is nearly identical to the process of retrieval using GVSM in a matrix of user ratings. Using this observation, a model for filtering collaboratively using document content is possible.

REFERENCES

• 1: John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July 1998. Morgan Kaufman.
• 2: Jaime G. Carbonell, Yiming Yang, Robert E. Frederking, Ralf D. Brown, Yibing Geng, and Danny Lee. Translingual information retrieval: A comparative evaluation. In Proceedings of the 1997 International Joint Conference on Artifical Intelligence (IJCAI '97), Nagoya, Japan, August 1997.
• 3: Joseph A. Konstan , Bradley N. Miller , David Maltz , Jonathan L. Herlocker , Lee R. Gordon , John Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, v.40 n.3, p.77-87, March 1997 [doi>10.1145/245108.245126]
• 4: Michael L. Littman and Fan Jiang. A comparison of two corpus-based methods for translingual information retrieval. Technical Report CS-1998-11, Department of Computer Science, Duke University, 1998.
• 5: Gerard Salton and Chris Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41:288-297, 1990.
• 6: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: User profiles and alert services

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Design, Human Factors, Theory