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A recommending system for mobile games using the dynamic nonparametric model

Author

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  • Bae, Joonho
  • Park, Jinkyoo
  • Choi, Jeonghye
  • Bum Soh, Seung

Abstract

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully dynamic Bayesian approach into the traditional collaborative filtering and hence captures the change of customer preferences in the mobile game industry with higher flexibility. Using the extensive log data of mobile gamers, we demonstrate that our DNP model closely predicts game usage patterns with higher accuracy than other widely used benchmarks. We also present various applications that reveal the managerial relevance of our model. Personalized recommendation lists of games generated by the DNP model outperform those by benchmarks in terms of adoption rates and repeated usage. Our model can also be used to improve the prediction performance of future in-app purchases and to decide the optimal size of recommendation sets based on types of gamers.

Suggested Citation

  • Bae, Joonho & Park, Jinkyoo & Choi, Jeonghye & Bum Soh, Seung, 2023. "A recommending system for mobile games using the dynamic nonparametric model," Journal of Business Research, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:jbrese:v:167:y:2023:i:c:s014829632300437x
    DOI: 10.1016/j.jbusres.2023.114079
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    References listed on IDEAS

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