论文标题
关于随机试验的公平性，以推荐异质人口统计以及其他
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond
论文作者
论文摘要
推荐中观察到的事件是政策决定的结果，因此它们通常被选择性地标记，即数据并非随机缺失（MNAR），这通常会导致对真实结果风险的估计，这通常会引起很大的偏见。一种纠正MNAR偏置的一般方法正在执行小型随机对照试验（RCT），其中采用额外的统一策略将项目随机分配给每个用户。在这项工作中，我们专注于同质和异质人口统计学的RCT的公平性，尤其是分析后一种情况下最不利的群体的偏见。考虑到RCT的局限性，我们提出了一种新颖的反事实鲁棒风险最小化（CRRM）框架，该框架完全没有昂贵的RCT，并得出其理论概括误差。最后，对合成任务和现实世界数据集进行了经验实验，证明了我们方法在公平性和概括方面的优势。
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk. A general approach to correct MNAR bias is performing small Randomized Controlled Trials (RCTs), where an additional uniform policy is employed to randomly assign items to each user. In this work, we concentrate on the fairness of RCTs under both homogeneous and heterogeneous demographics, especially analyzing the bias for the least favorable group on the latter setting. Considering RCTs' limitations, we propose a novel Counterfactual Robust Risk Minimization (CRRM) framework, which is totally free of expensive RCTs, and derive its theoretical generalization error bound. At last, empirical experiments are performed on synthetic tasks and real-world data sets, substantiating our method's superiority both in fairness and generalization.
