提出防欺诈的订阅平台收入分配新规则，让作弊者无利可图。
Fraud-Proof Revenue Division on Subscription Platforms
- 设计三种抗操纵的分配机制公理，评估现有规则可靠性。
- 发现主流平台算法既防不住骗，还让检测变得计算上不可行。
- 新规则ScaledUserProp满足所有公理，实证更公平合理。
我们研究订阅制平台模型，用户支付固定费用获取内容无限访问权，创作者按比例分得收入。现有欺诈检测多依赖机器学习，与作恶者陷入持续对抗。本文探索能天然抑制操纵行为的收入分配机制，形式化三种抗操纵公理，并检验现有规则是否满足。结果表明，广泛使用的主流分配机制不仅无法防止欺诈，反而使操纵检测在计算上不可行。为此，我们提出新规则ScaledUserProp，满足全部三项公理。真实与合成流媒体数据实验表明，该规则相较现有方法更具公平性。
原文摘要 · Abstract (English)
We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.
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