边缘敏感的公平排序算法
Marginal-Certainty-aware Fair Ranking Algorithm
摘要：
排名系统在现代互联网服务中无处不在，包括在线市场、社交媒体和搜索引擎。传统的排名系统只关注如何获得更好的相关性估计。当相关性估计可用时，它们通常采用以用户为中心的优化策略，其中通过根据项目的估计相关性对项目进行排序来生成排名列表。然而，这种以用户为中心的优化忽略了一个事实，即项目提供商也从排名系统中获取效用。现有研究表明，这种以用户为中心的优化会对物品提供者造成很大的不公平，其次是对物品提供者的不公平机会和不公平的经济收益。为了解决排名公平性，已经提出了许多公平排名方法。然而，正如我们在本文中所示，这些方法可能是次优的，因为它们直接依赖于相关性估计而不知道不确定性（即，估计的相关性的方差）。为了解决这种不确定性，我们提出了一种新的边缘感知公平算法MCFair。MCFair联合优化公平性和用户效用，而相关性估计以在线方式不断更新。在MCFair中，我们首先开发了一个排名目标，包括不确定性，公平性和用户效用。然后我们直接使用排序目标的梯度作为排序得分。我们从理论上证明了基于梯度的MCFair对于上述排名目标是最优的。从经验上讲，我们发现，在半合成数据集上，MCFair是有效和实用的，与最先进的公平排名方法相比，可以提供更优越的性能。为了便于重现，我们发布了我们的代码https://github.com/Taosheng-ty/WSDM22-MCFair。
英文摘要：
Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get better relevance estimation. When relevance estimation is available, they usually adopt a user-centric optimization strategy where ranked lists are generated by sorting items according to their estimated relevance. However, such user-centric optimization ignores the fact that item providers also draw utility from ranking systems. It has been shown in existing research that such user-centric optimization will cause much unfairness to item providers, followed by unfair opportunities and unfair economic gains for item providers. To address ranking fairness, many fair ranking methods have been proposed. However, as we show in this paper, these methods could be suboptimal as they directly rely on the relevance estimation without being aware of the uncertainty (i.e., the variance of the estimated relevance). To address this uncertainty, we propose a novel Marginal-Certainty-aware Fair algorithm named MCFair. MCFair jointly optimizes fairness and user utility, while relevance estimation is constantly updated in an online manner. In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility. Then we directly use the gradient of the ranking objective as the ranking score. We theoretically prove that MCFair based on gradients is optimal for the aforementioned ranking objective. Empirically, we find that on semi-synthesized datasets, MCFair is effective and practical and can deliver superior performance compared to state-of-the-art fair ranking methods. To facilitate reproducibility, we release our code https://github.com/Taosheng-ty/WSDM22-MCFair.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
