Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favorite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort\footnote{\textbf{Reproducibility:}The code and datasets are available at \url{https://github.com/13543024276/FairSort}} to find a trade-off solution among user-side fairness, provider-side fairness, and personalized recommendations utility. Previous works habitually treat this issue as a knapsack problem, incorporating both-side fairness as constraints. In this paper, we adopt a novel perspective, treating each recommendation list as a runway rather than a knapsack. In this perspective, each item on the runway gains a velocity and runs within a specific time, achieving re-ranking for both-side fairness. Meanwhile, we ensure the Minimum Utility Guarantee for personalized recommendations by designing a Binary Search approach. This can provide more reliable recommendations compared to the conventional greedy strategy based on the knapsack problem. We further broaden the applicability of FairSort, designing two versions for online and offline recommendation scenarios. Theoretical analysis and extensive experiments on real-world datasets indicate that FairSort can ensure more reliable personalized recommendations while considering fairness for both the provider and user.

翻译：传统推荐系统通过向用户推荐其偏好的项目来最大化用户满意度。这种以用户为中心的方法可能导致提供者之间的曝光分布不公平。相反，以提供者为中心的设计则可能对用户产生不公。因此，本文提出一种重排序模型FairSort\footnote{\textbf{可复现性：}代码与数据集可在\url{https://github.com/13543024276/FairSort}获取}，以在用户侧公平性、提供者侧公平性与个性化推荐效用之间寻求平衡解。先前研究常将此问题视为背包问题，并将双边公平性作为约束条件处理。本文采用一种新颖视角，将每个推荐列表视为跑道而非背包。在此视角下，跑道上的每个项目获得速度并在特定时间内运行，从而实现兼顾双边公平性的重排序。同时，我们通过设计二分搜索方法确保个性化推荐的最小效用保证。相较于基于背包问题的传统贪心策略，该方法能提供更可靠的推荐。我们进一步拓展了FairSort的适用性，针对在线与离线推荐场景分别设计了两个版本。理论分析及在真实数据集上的大量实验表明，FairSort在兼顾提供者与用户公平性的同时，能够确保更可靠的个性化推荐。