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This study introduces a dynamic graph model to predict user churn in online games, outperforming static models by capturing evolving player interactions. This dynamic approach improves churn prediction accuracy in fast-paced gaming environments.

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Area of Science:

  • Machine Learning
  • Data Science
  • Game Analytics

Background:

  • User churn prediction is crucial in the gaming industry.
  • Existing Graph Neural Network (GNN) models use static graphs, failing to capture dynamic user interactions.
  • Massively multiplayer online role-playing games (MMORPGs) exhibit complex, evolving user relationships.

Purpose of the Study:

  • To propose a dynamic graph model for predicting user churn in games.
  • To effectively capture temporal changes in user behavior and interactions.
  • To improve the accuracy of churn prediction in dynamic gaming environments.

Main Methods:

  • Developed a dynamic graph model to represent user interactions over time.
  • Utilized data from 10,000 users of the MMORPG 'Blade & Soul'.
  • Compared the proposed dynamic model against conventional algorithms and static graph models.

Main Results:

  • The dynamic graph model achieved a higher F1 score than conventional and static graph models.
  • Dynamic graphs demonstrated a superior ability to reflect changes in user behavior.
  • The model effectively predicted user churn based on evolving interaction patterns.

Conclusions:

  • Dynamic graph models are more effective than static models for churn prediction in interactive domains like gaming.
  • The proposed model offers a significant advancement in understanding and predicting user churn in the gaming industry.
  • Capturing the dynamic nature of user interactions is key to accurate churn prediction.