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Mining Habitual User Choices from Google Maps History Logs

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Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Nowadays, many people use their smart-phones for voice guided navigation. Such smart-phone applications provide up-to-date information about traffic and congestion and assist driving by giving route alternatives and estimations on the arrival time. In the same time, they record users’ position information and extract simple rules for them, such as the time they commute or return home. In this work, we extend this simple analysis in order to identify more habitual behaviors, based on the analysis of user’s location data. More specifically, we analyze user’s GPS logs provided through his Google location history, and find locations that user usually spends more time, and types of places that the user visits in a regular base (such as cinemas, restaurants, gyms, bars etc). In addition to this, we analyze the most frequent user routes (e.g. from home to work and back) and employing a trajectory partitioning methodology, we identify the most frequent sub-trajectories followed by the user. We further associate frequent sub-trajectories in order to form alternative routes for users’ favorite routes using a route reconstruction algorithm. The information about users’ places of interest and users’ alternative routes can be exploited by voice assistants, recommendation systems and navigation systems in order to improve user-experience.

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Notes

  1. 1.

    https://www.businessinsider.com/voice-assistants-car-vs-smartphones-2018-11.

  2. 2.

    https://github.com/metemaad/TrajLib.

  3. 3.

    https://github.com/Pent00/YenKSP.

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Acknowledgements

This work has been developed in the frame of the MASTER project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 777695.

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Authors and Affiliations

  1. Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece

    Iraklis Varlamis, Christos Sardianos & Grigoris Bouras

Authors
  1. Iraklis Varlamis
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  2. Christos Sardianos
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  3. Grigoris Bouras
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Corresponding author

Correspondence to Iraklis Varlamis .

Editor information

Editors and Affiliations

  1. Computer Engineering, Fırat University, Elazığ, Turkey

    Mehmet Kaya

  2. Ministry of Health, Ankara, Turkey

    Şuayip Birinci

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jalal Kawash

  4. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

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Varlamis, I., Sardianos, C., Bouras, G. (2020). Mining Habitual User Choices from Google Maps History Logs. In: Kaya, M., Birinci, Ş., Kawash, J., Alhajj, R. (eds) Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-33698-1_9

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