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Modeling mobile apps user behavior using Bayesian networks

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Abstract

Modern apps based businesses are increasingly interested in data driven decision making to achieve business goals as well as retaining their customer base. In this paper, we propose a Bayesian network approach to assess the mobile apps user behavior. We propose a strategy to build Bayesian networks and further improve the causal networks using re-sampling methods to best represent the causal representation between app user retention and in-app features. Structural hamming distances (SHD) are then used for assessing similar Bayesian network structures learned using data available from a local mobile app developing company. We also conduct a simulation study to assess the effect of re-sampling techniques towards the Bayesian network performance with various learning algorithms.

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Authors and Affiliations

  1. Department of Statistics, University of Manitoba, Winnipeg, MB, R3T2N2, Canada

    Isuru Dharmasena & Saman Muthukumarana

  2. Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T2N2, Canada

    Mike Domaratzki

Authors
  1. Isuru Dharmasena
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  2. Mike Domaratzki
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  3. Saman Muthukumarana
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Correspondence to Saman Muthukumarana.

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Dharmasena, I., Domaratzki, M. & Muthukumarana, S. Modeling mobile apps user behavior using Bayesian networks. Int. j. inf. tecnol. 13, 1269–1277 (2021). https://doi.org/10.1007/s41870-021-00699-7

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