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Socio-technical Design of Ubiquitous Computing Systems pp 131–150Cite as

Collaborative Context Prediction

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Abstract

Context prediction is used to proactively adapt e.g., services to users’ needs. Due to the fact that context prediction enables proactiveness it has a high significance for UC systems. To the best of our knowledge, research literature on context prediction only focuses on the history of the user whose next context has to be predicted. Does a user suddenly change her behaviour in an unexpected way, the context history of the user does not contain appropriate context information to provide reliable context predictions. Hence, context prediction algorithms will fail to predict a user’s next context if they solely rely on the context history of the user, whose context has to be predicted. To overcome the gap of missing context information in the user’s context history, the Collaborative Context Prediction (CCP) approach is proposed. CCP takes advantage of existing direct and indirect relations which may exist among the context histories of various users. Thereby, CCP bases on the Higher-order Singular Value Decomposition, which is also applied in the field of recommendation systems. To provide an evaluation of CCP it is compared to state-of-the-art context prediction approaches with respect to its prediction accuracy using a collaborative data set. For the reason that context prediction approaches primarily use personal context data legal criteria are presented. These criteria are used to legally assess the context prediction approaches. Subsequently, the resulting consequences are discussed.

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Notes

  1. 1.

    http://www.last.fm.

  2. 2.

    http://www.flickr.com/.

  3. 3.

    http://www.pervasive.jku.at/Research/Context_Database/.

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Authors and Affiliations

  1. Communication Technology, Kassel University, Pfannkuchstr. 1, 34121, Kassel, Germany

    Christian Voigtmann & Klaus David

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  1. Christian Voigtmann
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  2. Klaus David
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Corresponding author

Correspondence to Christian Voigtmann .

Editor information

Editors and Affiliations

  1. University of Kassel, Kassel, Germany

    Klaus David

  2. University of Kassel, Kassel, Germany

    Kurt Geihs

  3. University of Kassel, Kassel, Germany

    Jan Marco Leimeister

  4. University of Kassel, Kassel, Germany

    Alexander Roßnagel

  5. University of Kassel, Kassel, Germany

    Ludger Schmidt

  6. University of Kassel, Kassel, Germany

    Gerd Stumme

  7. University of Kassel, Kassel, Germany

    Arno Wacker

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Voigtmann, C., David, K. (2014). Collaborative Context Prediction. In: David, K., et al. Socio-technical Design of Ubiquitous Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-05044-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-05044-7_8

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