一次训练实现推荐系统公平性动态调节，无需重训
Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation
- 共享表示层+条件适配器，支持不同公平性水平的用户嵌入
- 实测在多数据集上达成优于或相当的公平-精度平衡
- 适合需要灵活调整公平标准的推荐系统部署场景
尽管已有诸多缓解推荐系统不公平性的努力，现有方法通常在训练时固定公平性要求，缺乏训练后的灵活性。但在真实场景中，不同利益相关方可能随时间提出不同的公平性需求，重新训练以适应新要求成本过高。为此，我们提出 Cofair，一种单次训练即可实现推荐系统后训练公平性控制的框架。Cofair 引入带有公平性条件的适配模块的共享表示层，生成针对不同公平性水平的用户嵌入，并设计用户级正则项，确保各公平性水平下用户层面的公平性单调提升。理论证明，其对抗目标上界为群体均等性，正则项保障用户层级的渐进式公平。在多个数据集和主干模型上的实验表明，该框架可在不重新训练的情况下实现多层级动态公平性，公平性-准确性曲线表现优于或相当当前最优基线。
原文摘要 · Abstract (English)
Despite growing efforts to mitigate unfairness in recommender systems, existing fairness-aware methods typically fix the fairness requirement at training time and provide limited post-training flexibility. However, in real-world scenarios, diverse stakeholders may demand differing fairness requirements over time, so retraining for different fairness requirements becomes prohibitive. To address this limitation, we propose Cofair, a single-train framework that enables post-training fairness control in recommendation. Specifically, Cofair introduces a shared representation layer with fairness-conditioned adapter modules to produce user embeddings specialized for varied fairness levels, along with a user-level regularization term that guarantees user-wise monotonic fairness improvements across these levels. We theoretically establish that the adversarial objective of Cofair upper bounds demographic parity and the regularization term enforces progressive fairness at user level. Comprehensive experiments on multiple datasets and backbone models demonstrate that our framework provides dynamic fairness at different levels, delivering comparable or better fairness-accuracy curves than state-of-the-art baselines, without the need to retrain for each new fairness requirement. Our code is publicly available at https://github.com/weixinchen98/Cofair.
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