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)


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.


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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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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