相关实验视频
Updated: Jun 9, 2025

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Measuring Engagement of Spectators of Social Digital Games
Published on: July 3, 2021
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解读显式和隐式特征,以在线视频游戏中可靠,可解释和可操作的用户流量预测
IEEE transactions on visualization and computer graphics
|October 29, 2024
概括
这项研究引入了一种新的方法,通过使机器学习模型更易于理解来预测在线游戏中的玩家流失率. 它有助于游戏设计师和产品经理解释模型特征,以获得更好的玩家保留策略.
科学领域:
- 计算机科学 计算机科学
- 人与计算机的交互
- 游戏研究 游戏研究
背景情况:
- 在线视频游戏行业面临着激烈的竞争,推动了对玩家保留策略的需求.
- 机器学习 (ML) 模型越来越多地用于预测玩家流失,但它们的"黑子"性质阻碍了领域专家的采用.
- 现有的可解释的人工智能 (XAI) 技术正在努力弥合复杂模型和非技术游戏专业人员之间的差距.
研究的目的:
- 开发一种可靠和可解释的解决方案,用于预测在线游戏中的玩家流失率.
- 为非技术领域的专家,如游戏设计师和产品经理,增强对ML模型的理解.
- 提出XAI方法,提供可操作的见解,并确定玩家保留干预措施的关键特征.
主要方法:
- 将ML模型输入重组为明确和隐含的特征,以提高可解释性.
- 建立明确和隐含特征之间的联系,以帮助专家理解.
- 进行两项案例研究,包括专家反和主题内用户研究,以验证该方法.
主要成果:
- 拟议的方法有效地预测了玩家流失,同时提供了对模型决策的可解释的见解.
- 领域专家们对ML模型特征及其对玩家行为的影响有了更好的理解.
- 该方法成功地确定了针对目标球员保留干预的关键特征.
结论:
- 这项研究为游戏行业的玩家流失预测提供了一个新的,可解释的框架.
- 连接显式和隐式特征显著提高了非技术专家的ML模型的可用性.
- 开发的XAI方法提供了可操作的见解,促进了对球员保留的数据驱动决策.
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