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Inferring what a user is not interested in

  • Robert C. Holte
  • John Ng Yuen Yan
Applications I: Intelligent Information Filtering
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1081)

Abstract

This paper describes a system to improve the speed and success rate with which users browse software libraries. The system is a learning apprentice: it monitors the user's normal browsing actions and from these infers the goal of the user's search. It then searches the library being browsed, uses the inferred goal to evaluate items and presents to the user those that are most relevant. The main contribution of this paper is the development of rules for negative inference (i.e. inferring features that the user is not interested in). These produce a dramatic improvement in the system 's performance. The new system is more than twice as effective at identifying the user's search goal than the original, and it ranks the target much more accurately at all stages of search.

Keywords

Relevance Feedback Learn Agent Confidence Factor Plan Recognition Marked Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bos, E. (1992), “Some Virtues and Limitations Of Action Inferring Interfaces”, 5th Annual Symposium on User Interface Software and Technology.Google Scholar
  2. Cohen, R., and B. Spencer (1993), “Specifying and Updating Plan Libraries for Plan Recognition Tasks”, Proceedings of the Conference on Artificial Intelligence Applications (CAIA'93), pp. 27–33.Google Scholar
  3. Cypher, A. (1991), “EAGER: Programming Repetitive Tasks by Example”, SIGCHI'91, pp. 33–39.Google Scholar
  4. Dent, L., J. Boticario, J. McDermott, T.M. Mitchell and D. Zabowski (1992), “A Personal Learning Apprentice”, Proceedings of the 10th National Conference on Artificial Intelligence (AAAI'92), pp. 96–102.Google Scholar
  5. Drummond, C., D. Ionescu and R.C. Holte (1995), “A Learning Agent that Assists the Browsing of Software Libraries”, technical report TR-95-12, Computer Science Dept., University of Ottawa.Google Scholar
  6. Finin, T.W. (1983), “Providing Help and Advice in Task Oriented Systems”, IJCAI'83, pp. 176–178.Google Scholar
  7. Fischer, G., A. C. Lemke, T. Mastaglio, and A. I. Morch (1990), “Using Critics to Empower users”, Proceedings of CHI-90 (“Empowering People”), pp. 337–347.Google Scholar
  8. Goodman, B.A. and Diane J. Litman (1990), “Plan Recognition for Intelligent Interfaces”, CAIA'90, pp. 297–303.Google Scholar
  9. Haines, D., and W.B. Croft (1993), “Relevance Feedback and Inference Networks”, Proceedings of the 16th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2–11.Google Scholar
  10. Harman, D. (1992), “Relevance Feedback Revisited”, Proc. 15th International Conference on Research and Development in Information Retrieval (SIGIR'92), pp. 1–10.Google Scholar
  11. Hermens, L.A., and J.C. Schlimmer (1993), “A machine-learning apprentice for the completion of repetitive forms”, Proc. 9th Conference on Artificial Intelligence for Applications, pp. 164–170.Google Scholar
  12. Hook, K., J. Karlgren, and A. Woern (1993), “Inferring Complex Plans”, Intelligent User Interfaces, pp. 231–234.Google Scholar
  13. Jennings, A., and H. Higuchi (1993), A User Model Neural Network for a Personal News Service, User Modeling and User-Adapted Interaction, vol. 3, pp. 1–25.Google Scholar
  14. Lang, Ken (1995), “NewsWeeder: Learning to Filter Netnews”, Proceedings of the 12th International Conference on Machine Learning, Morgan Kaufmann, pp. 331–339.Google Scholar
  15. Maes, P., and R. Kozierok (1993), “Learning Interface Agents”, AAAI'93, pp. 459–465.Google Scholar
  16. McCalla, G., J. Greer, and R. Coulman (1992), “Enhancing the Robustness of Model-Based Recognition”, Proceedings of 3rd International Workshop on User Modelling, pp. 240–248.Google Scholar
  17. Mitchell, T.M., S. Mahadevan and L. Steinberg (1985), “LEAP: A Learning Apprentice for VLSI Design”, IJCAI'85, pp. 573–580.Google Scholar
  18. Schlimmer, J.C. and L.A. Hermens, (1993), Software Agents: Completing Patterns and Constructing User Interfaces, Journal of Artificial Intelligence Research, vol. 1, pp. 61–89.Google Scholar
  19. Sheth, B. and P. Maes (1993), “Evolving Agents For Personalized Information Filtering”, CAIA'93, pp. 345–352.Google Scholar
  20. Silverman, B.G. and T.M. Mazher (1992), Expert Critics in Engineering Design: Lessons Learned and Research Needs, AI Magazine, vol. 13, no. 1, pp. 45–62.Google Scholar
  21. Witten, I.H. and Dan Mo (1993), “TELS: Learning Text Editing Tasks from Examples”, Watch What I Do, Alien Cypher (ed.), MIT Press, pp. 183–204.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Robert C. Holte
    • 1
  • John Ng Yuen Yan
    • 2
  1. 1.Computer Science DepartmentUniversity of OttawaOttawaCanada
  2. 2.BNR Ltd.OttawaCanada

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