In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.

翻译：本文从提供者角度解决推荐公平性问题，该问题在多利益相关者推荐系统中日益重要。现有关于提供者公平性的研究通常集中于设计比例公平性指标，该指标优先考虑系统整体公平性。然而社会学研究表明，为使市场更加稳定，最大最小公平性是更优的度量指标。其主要原因在于最大最小公平性优先提升最差提供者的效用，引导系统支持处于弱势市场地位的提供者。将最大最小公平性应用于推荐系统时，如何在在线推荐场景中平衡用户偏好与提供者公平性仍是一个具有挑战性的问题。本文提出了一种名为"提供者最大最小公平性重排序"（P-MMF）的在线重排序模型来解决该问题。具体而言，P-MMF将提供者公平推荐形式化为资源分配问题，其中曝光位视为待分配给提供者的资源，并在分配过程中采用最大最小公平性作为正则化项。研究表明该问题可进一步表示为正则化在线优化问题，并在其对偶空间内高效求解。在在线重排序阶段，设计动量梯度下降方法实现动态重排序。理论分析表明P-MMF的遗憾值有界。在四个公开推荐数据集上的实验结果表明，P-MMF能够优于现有最先进的基线方法。实验结果还显示，P-MMF在包含大量项目的语料库上保持较低的计算成本。