In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications. Recommender systems are prominent examples of these machine learning (ML) systems that aid users in making decisions. The majority of past literature research on RS fairness treats user and item fairness concerns independently, ignoring the fact that recommender systems function in a two-sided marketplace. In this paper, we propose CP-FairRank, an optimization-based re-ranking algorithm that seamlessly integrates fairness constraints from both the consumer and producer side in a joint objective framework. The framework is generalizable and may take into account varied fairness settings based on group segmentation, recommendation model selection, and domain, which is one of its key characteristics. For instance, we demonstrate that the system may jointly increase consumer and producer fairness when (un)protected consumer groups are defined on the basis of their activity level and main-streamness, while producer groups are defined according to their popularity level. For empirical validation, through large-scale on eight datasets and four mainstream collaborative filtering (CF) recommendation models, we demonstrate that our proposed strategy is able to improve both consumer and producer fairness without compromising or very little overall recommendation quality, demonstrating the role algorithms may play in avoiding data biases.

翻译：近年来，人们日益认识到，当机器学习算法被用于自动化决策时，可能对个人或群体造成不公，从而产生法律、伦理或经济影响。推荐系统作为辅助用户决策的典型机器学习系统，是这类问题的突出案例。过去大部分关于推荐系统公平性的文献研究，将用户公平性与项目公平性问题独立处理，忽略了推荐系统在双边市场中运作的事实。本文提出CP-FairRank——一种基于优化的重排序算法，通过联合目标框架无缝整合消费者与生产者双方的公平性约束。该框架具有通用性，能根据群体划分、推荐模型选择及领域等不同因素，灵活适配多样化的公平性设置，这恰是其核心特性之一。例如，我们证明：当（非）受保护消费者群体按活跃度与主流度划分，而生产者群体按流行度划分时，该系统可同步提升消费者与生产者的公平性。通过基于八个数据集和四种主流协同过滤推荐模型的大规模实证验证，我们证明所提策略能在不降低或极小损失整体推荐质量的前提下，同时改善消费者与生产者的公平性，凸显算法在避免数据偏见中的潜在作用。