FairRec: 二元平台个性化推荐中的双面公平性
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
AI总结：
FairRec通过确保生产者和客户双方的公平性，在双面平台中实现个性化推荐的公平性，同时保持推荐质量的轻微损失。
摘要：
我们研究了在双面在线平台背景下公平推荐问题，其中包括一方的客户和另一方的生产者。传统上，这些平台的推荐服务专注于通过根据个体客户的个性化偏好定制结果来最大化客户满意度。然而，我们的研究发现，这种以客户为中心的设计可能导致生产者之间的曝光分配不公，这可能会对其福祉产生负面影响。另一方面，以生产者为中心的设计可能会对客户造成不公平。因此，我们考虑了涵盖客户和生产者双方的公平性问题。我们的方法涉及将公平推荐问题映射到公平分配不可分割物品问题的一个受限版本。我们提出的FairRec算法保证了大多数生产者至少获得最大最小份额（MMS）的曝光，并为每个客户保证至少 envy-free up to one item（EF1）的公平性。在多个真实世界数据集上的广泛评估显示，FairRec在确保双面公平性的同时，仅导致整体推荐质量的轻微损失。
英文摘要：
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.
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。
