Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, however, mainly focus on maximizing consumers' satisfaction by recommending the most relevant items to each individual. This may result in unfair exposure of items, thus jeopardizing producer benefits. Additionally, they do not care whether consumers from diverse demographic groups are equally satisfied. To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee. We first propose four fairness constraints on consumers and producers. In order to train the whole framework in an end-to-end way, we utilize the smooth rank and stochastic ranking policy to make these fairness criteria differentiable and friendly to back-propagation. Then, we adopt the multiple gradient descent algorithm to generate a Pareto set of solutions, from which the most appropriate one is selected by the Least Misery Strategy. The experimental results demonstrate that Multi-FR largely improves recommendation fairness on multiple stakeholders over the state-of-the-art approaches while maintaining almost the same recommendation accuracy. The training efficiency study confirms our model's ability to simultaneously optimize different fairness constraints for many stakeholders efficiently.

翻译：目前,大多数在线服务都以多利益攸关方市场为主,消费者和生产者的目标可能不同。常规建议系统主要侧重于通过向每个人推荐最相关的项目,最大限度地提高消费者的满意度。这可能导致产品受到不公平的暴露,从而损害生产者的利益。此外,它们并不关心不同人口群体的消费者是否同样满意。为了解决这些限制,我们提议了一个多目标优化框架,即多目标框架,即公平意识建议,以适应性平衡各种利益攸关方的准确性和公平性,并有Pareto最佳性保证。我们首先提出对消费者和生产者的四种公平性限制。为了以端对端方式培训整个框架,我们利用平滑的等级和随机排序政策,使这些公平标准能够不同和有利于反调。然后,我们采用多重梯度下降算法,以产生一套解决方案,最合适的解决方案由最小的错误战略从中挑选。实验结果表明,多利益攸关方在很大程度上提高了对州级和生产者的公平性建议。我们利用平整的等级和随机分级政策,同时保持了这些公平性。