解决匹配平台因曝光偏差导致的推荐不公问题。
Counterfactual Reciprocal Recommender Systems for User-to-User Matching
- 基于反事实因果框架，用逆倾向评分校正数据偏差。
- 提升长尾用户覆盖率达51%，曝光不平等降低24%。
- 适合需要公平匹配的社交、游戏、人才平台使用。
在约会、游戏和人才平台上，互惠推荐系统（RRS）要求双方互相接受才能匹配。然而，历史曝光策略导致热门用户数据被过度记录，形成反馈循环，影响学习效果与公平性。本文提出反事实互惠推荐系统（CFRR），一种因果框架以缓解此类偏差。CFRR采用逆倾向评分与自归一化目标函数。实验表明，CFRR将NDCG@10提升最高达3.5%（如在DBLP上从0.459升至0.475，在Synthetic上从0.299升至0.307），长尾用户覆盖率提升最高达51%（从0.504增至0.763），曝光不平等（Gini）降低最高24%（从0.708降至0.535）。该方法为更精准、更公平的用户匹配提供了有效路径。
原文摘要 · Abstract (English)
Reciprocal recommender systems (RRS) in dating, gaming, and talent platforms require mutual acceptance for a match. Logged data, however, over-represents popular profiles due to past exposure policies, creating feedback loops that skew learning and fairness. We introduce Counterfactual Reciprocal Recommender Systems (CFRR), a causal framework to mitigate this bias. CFRR uses inverse propensity scored, self-normalized objectives. Experiments show CFRR improves NDCG@10 by up to 3.5% (e.g., from 0.459 to 0.475 on DBLP, from 0.299 to 0.307 on Synthetic), increases long-tail user coverage by up to 51% (from 0.504 to 0.763 on Synthetic), and reduces Gini exposure inequality by up to 24% (from 0.708 to 0.535 on Synthetic). CFRR offers a promising approach for more accurate and fair user-to-user matching.
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