论文标题
根据不完整的暴露估计，暴露的公平性
Fairness of Exposure in Light of Incomplete Exposure Estimation
论文作者
论文摘要
暴露的公平性是对排名系统的公平性概念。它基于这样的想法，即所有项目或项目组应获得与项目的优点成正比的曝光或组中项目的集体优点。通常，随机排名政策用于确保暴露的公平性。以前的工作不切实际地假设我们可以可靠地估计由随机政策产生的每个排名中所有项目的预期暴露。在这项工作中，我们讨论了如何在政策包含排名的情况下，由于项目间依赖性而无法可靠地估计暴露分布的情况。在这种情况下，我们无法确定该政策是否可以公平。我们在本文中的贡献是双重的。首先，我们定义了一种称为Felix的方法，用于查找随机策略，该方法避免向用户显示未知曝光分布的排名，而无需损害用户实用程序或项目公平性。其次，我们扩展了对TOP-K设置的公平性研究，并在此环境中评估Felix。我们发现，与现有的公平排名方法相比，Felix可以显着减少未知曝光分布的排名数量，而不会下降用户效用或公平性，无论是全长和TOP-K排名而言。对于我们对用户行为不完全了解的情况，这是开发公平排名方法的重要第一步。
Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often, stochastic ranking policies are used to ensure fairness of exposure. Previous work unrealistically assumes that we can reliably estimate the expected exposure for all items in each ranking produced by the stochastic policy. In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution. In such cases, we cannot determine whether the policy can be considered fair. Our contributions in this paper are twofold. First, we define a method called FELIX for finding stochastic policies that avoid showing rankings with unknown exposure distribution to the user without having to compromise user utility or item fairness. Second, we extend the study of fairness of exposure to the top-k setting and also assess FELIX in this setting. We find that FELIX can significantly reduce the number of rankings with unknown exposure distribution without a drop in user utility or fairness compared to existing fair ranking methods, both for full-length and top-k rankings. This is an important first step in developing fair ranking methods for cases where we have incomplete knowledge about the user's behaviour.
