通过智能筛选避免误伤真粉丝，提升欺诈检测精准度。
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
- 用相似哈希分层采样+可插拔门控集成，从无标签数据中挑出可信的正常样本。
- 在真实数据上实现高精度与高召回，对用户和艺术家级欺诈都有效。
- 适合需要高可信负样本的反欺诈场景，尤其对抗伪装成真行为的恶意操作。
音乐流媒体欺诈通过人为刷播放量操纵排行榜和版税分配，严重威胁平台与创作者权益。传统方法难以区分真实极端情况（如超级粉丝、睡眠音乐）与有组织欺诈，因其行为模式高度相似。本文提出SAGE——一种具备反事实感知能力的负样本自动筛选方法，结合SimHash分层采样与模块化门控集成，从无标签数据中识别可信的正常样本。其集成架构支持可插拔的统计门（当前采用马氏距离与k-NN密度），并可通过配置投票阈值实现精度-召回率的灵活权衡。该方法通过底限约束采样解决了正-未标记学习中的表征偏差问题，确保稀有行为群体的全面覆盖。实验表明，在独立测试集上取得优异的精度与召回表现，且无需修改核心方法即可跨领域适用于用户级与艺术家级欺诈检测。
原文摘要 · Abstract (English)
Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and legitimate content creators. Traditional fraud detection approaches struggle with a critical challenge: many legitimate edge cases, including super-fans and sleep-music sessions, exhibit activity patterns that closely mimic those of coordinated fraud. We present SAGE, a novel counterfactual-aware negative harvesting approach that combines SimHash-based stratified sampling with a modular gating ensemble for confident negative identification from unlabeled data. Our ensemble architecture employs pluggable statistical gates (currently instantiated with Mahalanobis distance and k-NN density) with configurable voting thresholds enabling adaptive precision-recall trade-offs. This addresses the representation bias problem in Positive-Unlabeled learning by ensuring comprehensive coverage of rare behavioral cohorts through floor-constrained sampling. Evaluation demonstrates strong precision and recall on held-out data. The approach generalizes across fraud detection domains, achieving strong performance on both customer-level and artist-level fraud without modification to the core methodology.
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