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Sentient destination prediction

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Abstract

In addition to context awareness and proactive behaviour, personalization constitutes one of the most important factors to consider when building digital assistance systems. In the field of location prediction, personal information can lead to a significant improvement of the predictive performance of the respective models. However, most of the existing approaches handle this type of information separate from the actual location and movement data, making them incapable of taking the entire existing underlying dynamics into account. In the presented work, inspired by the Minsky’s frame system theory in the 1970s, we evaluate an adapted ontological construct, which we call context-specific cognitive frame (CSCF), in order to capture the entire experience of a user at a given moment. Moreover, we use CSCFs to associate situations that arise when visiting a certain location with the respective context information as well as the emotions and the personality of the user. We show that our method can be used to provide a flexible, more accurate and therefore more personalized user experience using the example of location prediction.

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Notes

  1. http://ntcir-lifelog.computing.dcu.ie/ and https://www.imageclef.org/2018/lifelog.

  2. http://ontologydesignpatterns.org/wiki/Submissions:N-Ary_Relation_Pattern_(OWL_2).

  3. https://www.w3.org/TR/owl-time/.

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Acknowledgements

The authors would like to thank all the diligently annotating participants of the user study.

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Authors and Affiliations

  1. Chassis Systems Control, Advance Engineering, Robert Bosch GmbH, Abstatt, Germany

    Antonios Karatzoglou

  2. Facebook, London, England

    Jan Ebbing

  3. Relex Solutions, Wiesbaden, Germany

    Phil Ostheimer

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Wenlan Hua & Michael Beigl

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  1. Antonios Karatzoglou
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Karatzoglou, A., Ebbing, J., Ostheimer, P. et al. Sentient destination prediction. User Model User-Adap Inter 30, 331–363 (2020). https://doi.org/10.1007/s11257-020-09257-5

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