揭露风险习惯:通过机器学习技术识别和预测问题徒
1Institute of Economics, Corvinus University of Budapest, Fővám tér 8, 1093, Budapest, Hungary.
Journal of gambling studies
|April 3, 2024
概括
这项研究引入了一种新的机器学习方法,用于检测有问题的徒,而不依赖于自我报告的数据. 这种新的无监督方法可以识别有风险的玩家进行实时干预,促进负责任的博.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 行为科学 行为科学
背景情况:
- 机器学习 (ML) 是为了识别问题徒而建立的.
- 当前的ML方法通常依赖于自我报告的数据,如帐户关闭或自我排除.
- 在无监督,实时识别问题博行为方面存在差距.
研究的目的:
- 开发一种新的ML方法,用于无监督识别有问题的徒.
- 为处于风险的玩家创建实时预测模型.
- 为促进负责任博的干预提供见解.
主要方法:
- 无人监督的学习技术,以产生问题徒的标签.
- 开发用于实时用户识别的预测模型.
- 联合无监督和监督的ML方法的验证.
主要成果:
- 成功生成了针对有问题的徒的无监督标签.
- 开发了准确的实时预测模型.
- 证明了新的综合方法的有效性.
结论:
- 拟议的方法为自我报告的标签提供了一个可行的替代方案,用于问题徒的识别.
- 实时检测可实现及时和有针对性的干预.
- 这种方法在促进负责任的博和更健康的玩家习惯方面具有重大潜力.
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