A Hybrid User Model for News Story Classification

  • Daniel Billsus
  • Michael J. Pazzani
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)

Abstract

We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user’s preferences and interests. In this paper we focus on the system’s user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of explicitly modeling information that the system has already presented to the user. This allows us to address an important issue that has thus far received virtually no attention in the Information Retrieval community: the fact that a user’s information need changes as a direct result of interaction with information. We evaluate the proposed algorithms on user data collected with a prototype of our system, and assess the individual performance contributions of both model components.

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References

  1. Allan, J., Carbonell, J., Doddington, G., Yamron, J. and Yang Y. (1998). Topic detection and tracking pilot study final report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia.Google Scholar
  2. Balabanovic, M. (1998). Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation. Ph.D. Thesis, Stanford University.Google Scholar
  3. Belkin, N. (1997). User modeling in Information Retrieval. Tutorial Overheads, available at http://www.scils.rutgers.edu/~belkin/um97oh/, Sixth International Conference on User Modeling, Chia Laguna, Sardinia.Google Scholar
  4. Belkin, N., Kay, J., Tasso, C. (eds) (1997). Special Issue on User Modeling and Information Filtering. User Modeling and User Adapted Interaction, 7(3).Google Scholar
  5. Chiu, B. and Webb, G. (1998). Using decision trees for agent modeling: improving prediction performance. User Modeling and User-Adapted Interaction 8:131–152.CrossRefGoogle Scholar
  6. Cohen, W. and Hirsh, H. (1998). Joins that generalize: text classification using WHIRL. In Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York, New York, 169–173.Google Scholar
  7. Duda, R., and Hart, P. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons, New York.MATHGoogle Scholar
  8. Lang, K. (1995). NewsWeeder: learning to filter news. Proceedings of the Twelfth International Conference on Machine Learning. Lake Tahoe, CA, 331–339.Google Scholar
  9. Lewis, D. and Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. London, Springer Verlag, 3–12.Google Scholar
  10. Maron, M. (1961). Automatic indexing: an experimental inquiry. Journal of the Association for Computing Machinery, 8:404–417.CrossRefMATHGoogle Scholar
  11. McCallum, A. and Nigam, K. (1998). A comparison of event models for naïve Bayes text classification. American Association for Artificial Intelligence (AAAI) Workshop on Learning for Text Categorization. Available as Technical Report WS-98–05, AAAI Press.Google Scholar
  12. Pazzani M., and Billsus, D. (1997). Learning and revising user profiles: the identification of interesting web sites. Machine Learning27, 313–331.CrossRefGoogle Scholar
  13. Salton, G. (1989). Automatic Text Processing. Addison-Wesley.Google Scholar
  14. Webb, G. (ed) (1998). Special Issue on Machine Learning for User Modeling. User Modeling and User-Adapted Interaction, 8(1–2).Google Scholar
  15. Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Manuscript submitted for publication.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Daniel Billsus
    • 1
  • Michael J. Pazzani
    • 1
  1. 1.Dept. of Information and Computer ScienceUniversity of CaliforniaIrvineUSA

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