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A Practical Activity Capture Framework for Personal, Lifetime User Modeling

  • Max Van Kleek
  • Howard E. Shrobe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

This paper addresses the problem of capturing rich, long-term personal activity logs of users’ interactions with their workstations, for the purpose of deriving predictive, personal user models. Our architecture addresses a number of practical problems with activity capture, including incorporating heterogeneous information from different applications, measuring phenomena with different rates of change, efficiently scheduling knowledge sources, incrementally evolving knowledge representations, and incorporating prior knowledge to combine low-level observations into interpretations better suited for user modeling tasks. We demonstrate that the computational and memory demands of general activity capture are well within reasonable limits even on today’s hardware and software platforms.

Keywords

User Modeling Resource Description Framework Observer Module Desktop Application Activity Capture 
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 2007

Authors and Affiliations

  • Max Van Kleek
    • 1
  • Howard E. Shrobe
    • 1
  1. 1.MIT Computer Science and, Artificial Intelligence Laboratory (CSAIL), 32 Vassar St., Cambridge, MA 02139 

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