  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.
