Policy Learning for Fairness in Ranking
Policy Learning for Fairness in Ranking
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发表时间：
2019-02
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通讯作者：
Ashudeep Singh;T. Joachims
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作者：
Ashudeep Singh;T. Joachims
传统的学习排名（LTR）方法优化了排名对用户的效用，但它们忽略了它们对排名项目的影响。然而，人们越来越认识到，对于广泛的排名应用（例如在线市场，就业安置，入学），后者是重要的考虑因素。为了满足这一需求，我们提出了一个通用的LTR框架，可以优化广泛的效用指标（如NDCG），同时满足公平的曝光约束的项目。该框架将可学习的排名函数扩展到随机排名策略，从而提供了一种严格表达公平性规范的语言。此外，我们提供了一个新的LTR算法称为公平PG排名直接搜索空间的公平排名政策，通过政策梯度的方法。除了推导框架和算法的理论证据之外，我们还提供了模拟和真实数据集的经验结果，验证了该方法在个人和群体公平性设置中的有效性。
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job placement, admissions). To address this need, we propose a general LTR framework that can optimize a wide range of utility metrics (e.g. NDCG) while satisfying fairness of exposure constraints with respect to the items. This framework expands the class of learnable ranking functions to stochastic ranking policies, which provides a language for rigorously expressing fairness specifications. Furthermore, we provide a new LTR algorithm called Fair-PG-Rank for directly searching the space of fair ranking policies via a policy-gradient approach. Beyond the theoretical evidence in deriving the framework and the algorithm, we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings.