Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where ($i$) the same provider is associated with multiple items of a list suggested to a user, ($ii$) an item is created by more than one provider jointly, and ($iii$) predicted user-item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user-item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility.

翻译：考虑到对物品提供者的建议的影响是多方推荐人系统的责任之一。项目提供者是在线平台的关键利益攸关方,其收入和计划受到其项目在推荐名单中收到的接触量的影响。先前的工作表明,某些具有共同敏感属性(如性别或种族)的少数群体正在受到间接和无意歧视的极大影响。我们本文的研究处理一种情况,即同一提供者与向用户推荐的多个清单项目相关联($2美元),一个项目是由一个以上提供者联合创建的,而其收入和计划则受到其项目在推荐名单中收到的接触量的影响。在此情况下,我们通过模拟少数群体在目录和互动中的各种表现,评估相关性、可见度和接触量的差异。根据出现的不公平结果,我们设计一种结合观察、抽样和损失规范的处理方法,同时学习用户项目相关性的分数。对现实世界数据的实验表明,我们的治疗导致不同的相关性较低。因此,建议的清单显示更公平的可见度和暴露度、少数群体项目覆盖率更高,以及可忽略损失的建议。