An Interpretable Classification Method Based on User Behaviour in Online Game

DOI

Bibliographic Information

Other Title
  • オンラインゲームにおけるユーザー行動特性に基づく意味解釈可能な分類手法の提案

Description

<p>For online game management, it is important to understand user behavioral characteristics. It is possible to increase user satisfaction for games by classification based on their behavioral characteristics and designing optimal game contents for each cluster having different types of motivation for games. We propose an interpretable classification method by using principal component analysis, K-means, and decision trees. As a result of applying the proposed method to actual in-game behavior data, we found user retention rate or Average Revenue Per User were different between the clusters. Furthermore, we confirmed that it could classify interpretable clusters and designed concrete game contents for each cluster.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390285300166399744
  • NII Article ID
    130007857389
  • DOI
    10.11517/pjsai.jsai2020.0_4j2gs203
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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