Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose \textbf{FARA}, a novel \textbf{F}uture-\textbf{A}ware \textbf{R}anking \textbf{A}lgorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Specifically, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods. We make our implementation public at \href{https://github.com/Taosheng-ty/QP_fairness/}{https://github.com/Taosheng-ty/QP\_fairness/}.

翻译：排序系统是现代信息检索（IR）应用（如搜索引擎和推荐系统）的关键组成部分。除排序对用户的相关性外，对物品提供者的曝光公平性也被视为排序优化的重要考量因素。目前已提出多种公平排序算法以联合优化排序相关性与公平性。然而，我们发现大多数现有公平排序方法采用贪心算法，仅针对下一轮会话或请求进行优化。如本文所示，这种短视范式可能限制排序优化的上限，并在长期导致次优性能。为此，我们提出\textbf{FARA}——一种新颖的\textbf{未来感知排序算法}，用于优化排序相关性与公平性。FARA并非贪心地优化下一轮会话的排序，而是通过联合优化多个排序列表并预存至未来会话中实现前瞻性规划。具体而言，FARA首先利用泰勒展开探究未来排序列表如何影响系统的整体公平性；随后基于泰勒展开分析，采用两阶段优化算法：首先求解最优未来曝光规划问题，再根据该最优规划构建排序列表。理论上，我们证明FARA对排序相关性与公平性联合优化具有最优性。实验方面，我们在三个半合成数据集上的大量结果表明，FARA高效、有效，且能显著优于现有最先进的公平排序方法。我们已在\href{https://github.com/Taosheng-ty/QP_fairness/}{https://github.com/Taosheng-ty/QP\_fairness/}公开实现代码。