论文标题
基于垂直分配的公平暴露在排名中摊销
Vertical Allocation-based Fair Exposure Amortizing in Ranking
论文作者
论文摘要
结果排名通常会影响消费者满意度以及每个物品在排名服务中获得的接触量。仅根据相关性对项目进行排名，从而最大程度地提高客户满意度，这将导致物品暴露的不公平分配，然后是商品生产商/提供商的不公平机会和经济收益。这种不公平将迫使提供商离开系统，并阻止新提供商进入。最终，消费者的购买选择量更少，消费者和提供商的公用事业将受到损害。因此，要在排名相关性和公平性之间保持平衡对双方至关重要。在本文中，我们专注于排名服务中的公平性。我们证明，现有的摊销公平优化方法在公平 - 相关的权衡方面可能是最佳的，因为它们无法利用消费者的先验知识。我们进一步提出了一种新型算法，称为基于垂直分配的公平敞口在排名或Verfair中摊销，以在暴露公平和排名绩效之间达到更好的平衡。在三个现实世界数据集上进行的广泛实验表明，Verfair在公平性表现折衷方面的最先进的公平排名算法从个人级别和小组级别上都取决于最先进的公平排名算法。
Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance will lead to unfair distribution of exposure for items, followed by unfair opportunities and economic gains for item producers/providers. Such unfairness will force providers to leave the system and discourage new providers from coming in. Eventually, fewer purchase options would be left for consumers, and the utilities of both consumers and providers would be harmed. Thus, to maintain a balance between ranking relevance and fairness is crucial for both parties. In this paper, we focus on the exposure fairness in ranking services. We demonstrate that existing methods for amortized fairness optimization could be suboptimal in terms of fairness-relevance tradeoff because they fail to utilize the prior knowledge of consumers. We further propose a novel algorithm named Vertical Allocation-based Fair Exposure Amortizing in Ranking, or VerFair, to reach a better balance between exposure fairness and ranking performance. Extensive experiments on three real-world datasets show that VerFair significantly outperforms state-of-the-art fair ranking algorithms in fairness-performance trade-offs from both the individual level and the group level.
