Automatically finding and recommending resources to support knowledge workers' activities

  • Authors:
  • Jianqiang Shen;Werner Geyer;Michael Muller;Casey Dugan;Beth Brownholtz;David R Millen

  • Affiliations:
  • Oregon State University, Corvallis, OR;IBM T.J. Watson Research, Cambridge, MA;IBM T.J. Watson Research, Cambridge, MA;IBM T.J. Watson Research, Cambridge, MA;IBM T.J. Watson Research, Cambridge, MA;IBM T.J. Watson Research, Cambridge, MA

  • Venue:
  • Proceedings of the 13th international conference on Intelligent user interfaces
  • Year:
  • 2008

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Abstract

Knowledge workers perform many different activities daily. Each activity defines a distinct work context with different information needs. In this paper we leverage users' activity representations, stored in an activity management system, to automatically recommend resources to support knowledge workers in their current activity. We developed a collaborative activity predictor to both predict the current work activity and measure a resource's relevance to a specific activity. Relevant resources are then displayed in a contextual side bar on the desktop. We describe the system, our new activity-centric search algorithm, and experimental results based on the data from 50 real users.