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Predicting Phone Usage Behaviors with Sensory Data Using a Hierarchical Generative Model

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9794))

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Abstract

Using a sizable set of sensory data and related usage records on Android devices, we are able to give a reasonable prediction of three imporant aspects of phone usage: messages, phone calls and cellular data. We solve the problem via an estimation of a user’s daily routine, on which we can train a hierarchical generative model on phone usages in all time slots of a day. The model generates phone usage behaviors in terms of three kinds of data: the state of user-phone interaction, occurrence times of an activity and the duration of the activity in each occurrence. We apply the model on a dataset with 107 frequent users, and find the prediction error of generative model is the smallest when compare with several other baseline methods. In addition, CDF curves illustrate the availability of generative model for most users with the distribution of prediction error for all test cases. We also explore the effects of time slots in a day, as well as size of training and test sets. The results suggest several interesting directions for further research.

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Authors and Affiliations

  1. Computer Science Department, Dartmouth College, Hanover, New Hampshire, 03755, USA

    Chuankai An & Dan Rockmore

Authors
  1. Chuankai An
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  2. Dan Rockmore
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Correspondence to Chuankai An .

Editor information

Editors and Affiliations

  1. New Mexico State University , Las Cruces, New Mexico, USA

    Huiping Cao

  2. University of Technology Sydney , Sydney, New South Wales, Australia

    Jinyan Li

  3. Massey University , Auckland, New Zealand

    Ruili Wang

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© 2016 Springer International Publishing Switzerland

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An, C., Rockmore, D. (2016). Predicting Phone Usage Behaviors with Sensory Data Using a Hierarchical Generative Model. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-42996-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42995-3

  • Online ISBN: 978-3-319-42996-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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