 A recommending system for mobile games using the dynamic nonparametric modelJoonho Bae, Jinkyoo Park, Jeonghye Choi and Seung Bum Soh Journal of Business Research, 2023, vol. 167, issue C 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. Keywords: Mobile application; Game log data; Tensor factorization; Gaussian process regression; Dynamic recommendation; Nonparametric model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:167:y:2023:i:c:s014829632300437x DOI: 10.1016/j.jbusres.2023.114079 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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