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Detecting real user tasks by training on laboratory contextual attention metadata

Rath, Andreas S. and Devaurs, Didier and Lindstaedt, Stefanie N. Detecting real user tasks by training on laboratory contextual attention metadata. (2009) In: Informatik 2009: Im Focus das Leben, Beiträge der 39. Jahrestagung der Gesellschaft für Informatik, 28 Sept - 02 Oct 2009, Lübeck, Germany . (Unpublished)

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Official URL: http://www.informatik2009.de/

Abstract

Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be achieved by training such classifiers offline on CAM gathered in laboratory settings. We also isolate a combination of metadata features that present a significantly better discriminative power than classical ones.

Item Type:Conference or Workshop Item (Paper)
Additional Information:This paper appears in GI Jahrestagung, volume 154 of LNI, page 1645-1653. GI, (2009), ISBN 978-3-88579-248-2
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Graz University of Technology - TU Graz (AUSTRIA)
Other partners > Know Center (AUSTRIA)
Laboratory name:
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Deposited By: Didier Devaurs
Deposited On:23 Jul 2013 10:10

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