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Jon Herlocker

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AAAI Conference 2005 System Paper

The TaskTracker System

  • Simone Stumpf
  • Anton Dragunov
  • Jon Herlocker
  • Lida Li

Knowledge workers spend the majority of their working hours processing and manipulating information. These users face continual costs as they switch between tasks to retrieve and create information. The TaskTracer project at Oregon State University investigates the possibilities of a desktop software system that will record in detail how knowledge workers complete tasks, and intelligently leverage that information to increase efficiency and productivity. Our approach assigns each observed user interface action to a task for which it is likely being performed. In this demonstration we show how we have applied machine learning in this environment.

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AAAI Conference 1999 Conference Paper

Combining Collaborative Filtering with Personal Agents for Better Recommendations

  • Nathaniel Good
  • J. Ben Schafer
  • Joseph A. Konstan
  • Al Borchers
  • Badrul Sarwar
  • Jon Herlocker
  • John Riedl
  • University of Minnesota

Information filtering agents and collaborative filtering both attempt to alleviate information overload by identifying which items a user will find worthwhile. Information filtering (IF) focuses on the analysis of item content and the development of a personal user interest profile. Collaborative filtering (CF) focuses on identification of other users with similar tastes and the use of their opinions to recommend items. Each technique has advantages and limitations that suggest that the two could be beneficially combined. This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone. It also shows that using CF to create a personal combination of a set of agents produces better results than either individual agents or other combination mechanisms. One key implication of these results is that users can avoid having to select among agents; they can use them all and let the CF framework select the best ones for them.

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