Driven by sustainability and economic considerations, two-sided recommendation platforms are required to satisfy the needs of both users and providers. Previous studies often indicate that the two sides' needs differ in urgency: providers have relatively long-term exposure requirements, while users desire short-term, accurate services. However, our empirical study reveals that existing methods for balancing fairness and accuracy often fail to ensure both long-term fairness and short-term accuracy under fluctuating user traffic in real applications. Notably, when user traffic is low, user experience tends to decline significantly. Then, we conducted a theoretical analysis confirming that user traffic is a crucial factor in such a trade-off problem. Ensuring accuracy and fairness under variable user traffic remains a challenge. Inspired by the bankruptcy problem in economics, we propose a novel fairness-aware re-ranking approach called BankFair. BankFair intuitively uses the Talmud rule to leverage periods of high user traffic to compensate for periods of low traffic, ensuring consistent user service while maintaining long-term fairness. BankFair is composed of two modules: (1) utilizing the Talmud rule to determine the necessary degree of fairness across varying user traffic periods, and (2) implementing an online re-ranking algorithm based on the fairness degree established by the Talmud rule. Experiments on one publicly available and one real industrial dataset demonstrate that BankFair outperforms all baselines in terms of both accuracy and provider fairness.

翻译：出于可持续性和经济性考量，双边推荐平台需同时满足用户与内容提供方的需求。既往研究常指出双方需求在紧迫性上存在差异：提供方通常具有相对长期的曝光需求，而用户则期望获得短期、精准的服务。然而，我们的实证研究表明，现有平衡公平性与准确性的方法在实际应用中往往难以在用户流量波动的条件下同时保障长期公平性与短期准确性。值得注意的是，当用户流量较低时，用户体验往往显著下降。随后，我们通过理论分析证实用户流量是此类权衡问题中的关键因素。如何在动态变化的用户流量下确保准确性与公平性仍是一个挑战。受经济学中破产问题的启发，我们提出一种新颖的公平感知重排序方法BankFair。该方法直观地运用塔木德规则，利用高用户流量时段补偿低流量时段，从而在维持长期公平性的同时保障用户服务的稳定性。BankFair由两个模块构成：（1）运用塔木德规则确定不同用户流量时期所需的公平性程度；（2）基于塔木德规则设定的公平性程度实施在线重排序算法。在一个公开数据集和一个真实工业数据集上的实验表明，BankFair在准确性和提供方公平性方面均优于所有基线方法。