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 FARA, a novel Future-Aware Ranking Algorithm 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. Particularly, 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.

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）的核心组件。除了对用户的排序相关性外，面向项目提供者的曝光公平性也被视为排序优化的重要因素。目前已提出诸多公平排序算法，以联合优化排序相关性与公平性。然而，我们发现现有大多数公平排序方法采用贪心算法，仅优化下一个即时会话或请求的排序结果。如本文所示，这种短视范式会限制排序优化的上界，导致长期次优性能。为此，我们提出FARA，一种用于排序相关性与公平性优化的新型未来感知排序算法。FARA并非贪心地优化下一个即时会话的排序结果，而是通过联合优化多个排序列表并将其存储用于未来会话，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将如何影响系统整体公平性；随后基于泰勒展开分析，采用两阶段优化算法：先求解最优未来曝光规划问题，再根据该规划构建最优排序列表。理论层面，我们证明FARA在排序相关性与公平性联合优化中具有最优性。实验层面，我们在三个半合成数据集上的大量实验表明，FARA高效且有效，相比现有最先进公平排序方法能显著提升排序性能。