TempODEGraphNet.zip
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Research on user churn prediction has been conducted across various domains for a long time. Among these, the gaming domain is characterized by its potential for diverse types of interactions between users. Due to this characteristic, many studies on churn prediction have considered the relationships between users and have primarily applied social network analysis. Recently, the use of Graph Neural Networks (GNNs) has been actively applied. However, existing studies utilizing GNNs have limitations as they use static graphs that do not effectively capture the dynamic nature of interactions that change over time. This study addresses these limitations by proposing a dynamic graph model for predicting user churn in games based on user interactions. Data are sourced from 10,000 users of 'Blade \& Soul' by NCSOFT. The proposed model effectively captures changes in user behavior over time and predicts user churn with a focus on interactions among users. Experimental results reveal that the proposed model achieves a higher F1 score compared with conventional algorithms and static graph models. Dynamic graphs more accurately reflect changes in user behavior compared with static graphs, particularly in domains with active interactions such as massively multiplayer online role-playing games. This work highlights the significance of user churn prediction in the gaming industry and demonstrates the effectiveness of the predictive models that use dynamic graphs.
用户流失预测研究长期以来已在多个领域广泛开展。其中，游戏领域的特点在于用户间存在丰富多样的交互形式。鉴于此特性，诸多流失预测研究均会考量用户间关系，并主要采用社交网络分析方法。近年来，图神经网络（Graph Neural Networks, GNNs）已被积极应用于相关研究。然而，现有采用图神经网络的研究存在局限性：其均使用静态图，无法有效捕捉随时间变化的交互动态特性。本研究针对上述局限，提出一种基于用户交互的动态图模型，用于游戏场景下的用户流失预测。实验数据取自NCsoft旗下游戏《剑灵》的10000名用户。所提模型可有效捕捉用户行为随时间的变化，并依托用户间交互关系完成用户流失预测。实验结果表明，相较于传统算法与静态图模型，所提模型的F1值更高。相较于静态图，动态图可更精准地反映用户行为变化，在大型多人在线角色扮演游戏这类交互活跃的领域中尤为如此。本研究凸显了用户流失预测在游戏行业的重要价值，并验证了采用动态图的预测模型的有效性。
