Artificial Intelligence Review

, Volume 13, Issue 5–6, pp 393–408 | Cite as

A Framework for Collaborative, Content-Based and Demographic Filtering

  • Michael J. Pazzani


We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.

collaborative filtering information filters recommendation systems 


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  1. Ali, K. and Pazzani, M. (1996). Error Reduction through Learning Multiple Descriptions. Machine Learning 24(3).Google Scholar
  2. Balabanovic, M. (1997). An Adaptive Web Page Recommendation Service. First International Conference on Autonomous Agents. CA: Marina del Rey.Google Scholar
  3. Balabanovic, M. and Shoham, Y. (1997). Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), March 97.Google Scholar
  4. Billsus, D. and Pazzani, M. (1998). Learning Collaborative Information Filters. In Shavlik, J. (ed.), Machine Learning: Proceedings of the Fifteenth International Conference. San Francisco, CA: Morgan Kaufmann Publishers.Google Scholar
  5. Blum, A. (1997). Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26: 5-23.Google Scholar
  6. Blum, A., Hellerstein, L. and Littlestone, N. (1995). Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. Journal of Computer and System Sciences 50(1): 32-40.Google Scholar
  7. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. and Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41: 391-407.Google Scholar
  8. Freund, Y., Iyer, R., Schapire, R. and Singer, Y. (1988). An Efficient Boosting Algorithm for Combining Preferences. In Shavlik, J. (ed.), Machine Learning: Proceedings of the Fifteenth International Conference. San Francisco, CA: Morgan Kaufmann Publishers.Google Scholar
  9. Hull, D., Pedersen, J. and Schütze, H. (1996). Method Combination for Document Filtering. Proceedings of the 19th International Conference on Research and Development in Information Retrieval, 279-287. Switzerland: Zurich.Google Scholar
  10. Kivinen, J. and Warmuth, M. (1995). Exponential Gradient Versus Gradient Descent for Linear Predictors. Information and Computation 132(1): 1-63.Google Scholar
  11. Krulwich, B. (1997). LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data. Artificial Intelligence Magazine 18(2): 37-45.Google Scholar
  12. Lang, K. (1995). NewsWeeder: Learning to Filter News. Proceedings of the Twelfth International Conference on Machine Learning. CA: Lake Tahoe.Google Scholar
  13. Larkey, L. and Croft, B. (1996). Combining Classifiers in Text Categorization in Proceedings of the 19th International Conference on Research and Development in Information Retrieval, 289-297. Switzerland: Zurich.Google Scholar
  14. Lewis, D., Schapire, R., Callan, J. and Papka, R. (1996). Training Algorithms for Linear Text Classifiers. In Hans-Peter Frei, Donna Harman, Peter Schauble and Ross Wilkinson (eds.), SIGIR '96: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 298-306.Google Scholar
  15. Lieberman, H. (1995). Letizia: An Agent that Assists Web Browsing. Proceedings of the International Joint Conference on Artificial Intelligence. Montreal.Google Scholar
  16. Littlestone, N. and Warmuth, M. (1994). The Weighted Majority Algorithm. Information and Computation 108(2): 212-261.Google Scholar
  17. Maes, P. (1994). Agents that Reduce Work and Information Overload. Communications of the ACM 37(7): 31-40.Google Scholar
  18. Miller, G. (1991). WordNet: An On-line Lexical Database. International Journal of Lexicography 3(4).Google Scholar
  19. Pazzani, M. and Billsus, D. (1997). Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27: 313-331.Google Scholar
  20. Press, W., Flannery, B., Teukolsky, S. and Vetterling, W. (1990). Numerical Recipes in C. Cambridge University Press.Google Scholar
  21. Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning 1: 81-106.Google Scholar
  22. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of Conference on Computer Supported Cooperative Work (CSCW'94), 175-186. Chapel Hill, NC: ACM Press.Google Scholar
  23. Rocchio, J. (1971). Relevance Feedback Information Retrieval. In Gerald Salton (ed.), The SMART Retrieval System-Experiments in Automated Document Processing, 313-323. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  24. Rumelhart, D., Hinton, G. and Williams, R. (1986). Learning Internal Representations by Error Propagation. In Rumelhart, D. and McClelland, J. (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, 318-362. Cambridge, MA: MIT Press.Google Scholar
  25. Salton, G. (1989). Automatic Text Processing. Addison-Wesley.Google Scholar
  26. Salton, G. and Buckley, C. (1990). Improving Retrieval Performance by Relevance Feedback. Journal of the American Society for Information Science 41: 288-297.Google Scholar
  27. Shardanand, U. and Maes, P. (1995). Social Information Filtering: Algorithms for Automating “Word of Mouth”. Proceedings of the Conference on Human Factors in Computing Systems (CHI'95), 210-217. Denver, CO: ACM.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Michael J. Pazzani
    • 1
  1. 1.Department of Information and Computer ScienceUniversity of CaliforniaIrvineUSA

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