FARE：面向公平曝光约束与不确定性感知的金融内容个性化深度强化学习
FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization
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中文总结 AI 辅助
FARE提出将SOV约束排序建模为深度强化学习，并引入CTR预测不确定性到策略设计，通过模块化执行层实现公平曝光，减少偏差同时最小化参与度损失。
中文摘要 AI 辅助
金融服务中的内容个性化系统必须确保不同产品之间的公平曝光——这一需求源于合同义务，以及防止“富者愈富”动态的必要性，在这种动态中，高点击率（CTR）的内容占据主导地位，而其他相关产品则获得极少的可见性。声量份额（SOV）约束通过保证每个内容类别在顶部位置曝光中占据目标比例，从而促进产品多样性和平衡的用户发现，解决了这一问题。虽然在CTR模型之上使用重排序层在实践中很常见，但我们提出了两个关键创新：（1）将SOV约束排序构建为深度强化学习问题，类似于算法金融中的约束交易执行；（2）明确将CTR预测不确定性纳入智能体的状态空间和策略设计中——使得对于高不确定性预测，可以进行更大的排序调整，因为偏离CTR最优排序的代价较小。我们引入了FARE（公平排序执行器），一个模块化的不确定性感知执行层，它可以将任何黑盒CTR模型的预测转换为SOV公平排序，而无需重新训练底层模型。我们的不确定性加权比例控制策略（FARE-PC）和学习型神经策略（FARE-ES、FARE-PPO）表明，不确定性感知方法可以大幅减少SOV与公平目标的偏差，同时最小化参与度损失，其中无梯度进化策略在合成数据上优于策略梯度方法，而在KuaiRand-Pure上排序则相反。
英文摘要
Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations and the need to prevent "rich-get-richer" dynamics where content with high click-through rate (CTR) dominates while other relevant products receive minimal visibility. Share of Voice (SOV) constraints, which guarantee each content category a target fraction of top-position exposure, address this by promoting product diversity and balanced user discovery. While re-ranking layers atop CTR models are common in practice, we propose two key novelties: (1) framing SOV-constrained ranking as a deep reinforcement learning problem analogous to constrained trade execution in algorithmic finance, and (2) explicitly incorporating CTR prediction uncertainty into the agent's state space and policy design-enabling larger ranking adjustments for high-uncertainty predictions where deviation from CTR-optimal ordering is less costly. We introduce FARE (Fair Ranking Executor), a modular uncertainty-aware execution layer that translates any black-box CTR model's predictions into SOV-fair rankings without retraining the underlying model. Our uncertainty-weighted proportional control policy (FARE-PC) and learned neural policies (FARE-ES, FARE-PPO) demonstrate that uncertainty-aware approaches can substantially reduce SOV deviation from fairness targets while minimizing engagement loss, with gradient-free evolution strategies outperforming policy gradient methods on synthetic data and the ordering reversing on KuaiRand-Pure.
