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.

本文主要研究多边推荐系统中的提供方，其中少数特定群体存在不公平的现象，通过模拟不同提供方群体的交互，提出了一种处理方法，结果显示推荐列表的可见性和覆盖率更公平，推荐效用下降微不足道。

Interplay between Upsampling and Regularization for Provider Fairness in  Recommender Systems

Anti-discrimination is an increasingly important task in data science. In
this paper, we investigate the problem of discovering both direct and indirect
discrimination from the historical data, and removing the discriminatory
effects before the data is used for predictive analysis (e.g., building
classifiers). We make use of the causal network to capture the causal structure
of the data. Then we model direct and indirect discrimination as the
path-specific effects, which explicitly distinguish the two types of
discrimination as the causal effects transmitted along different paths in the
network. Based on that, we propose an effective algorithm for discovering
direct and indirect discrimination, as well as an algorithm for precisely
removing both types of discrimination while retaining good data utility.
Different from previous works, our approaches can ensure that the predictive
models built from the modified data will not incur discrimination in decision
making. Experiments using real datasets show the effectiveness of our
approaches.

本文通过利用因果网络捕捉数据的因果结构，将直接和间接歧视建模为路径特异性效应，并提出了一种有效的算法，用于发现和消除直接和间接歧视，以及保留数据效用。实验结果表明，该方法有效性较高。