Personalized pricing assigns different prices to customers for the same product based on customer-specific features to improve retailer revenue. However, this practice often raises concerns about fairness at both the individual and group levels. At the individual level, a customer may perceive unfair treatment if he/she notices being charged a higher price than others. At the group level, pricing disparities can result in discrimination against certain protected groups, such as those defined by gender or race. Existing studies on fair pricing typically address individual and group fairness separately. This paper bridges the gap by introducing a new formulation of the personalized pricing problem that incorporates both dimensions of fairness in social network settings. To solve the problem, we propose FairPricing, a novel framework based on graph neural networks (GNNs) that learns a personalized pricing policy using customer features and network topology. In FairPricing, individual perceived unfairness is captured through a penalty on customer demand, and thus the profit objective, while group-level discrimination is mitigated using adversarial debiasing and a price regularization term. Unlike existing optimization-based personalized pricing, which requires re-optimization whenever the network updates, the pricing policy learned by FairPricing assigns personalized prices to all customers in an updated network based on their features and the new network structure, thereby generalizing to network changes. Extensive experimental results show that FairPricing achieves high profitability while improving individual fairness perceptions and satisfying group fairness requirements.

翻译：个性化定价依据顾客特定特征对同一商品设定不同价格以提升零售商收益，但这一做法常引发个体与群体层面的公平性担忧。在个体层面，当顾客察觉自身被收取高于他人的价格时，可能产生不公平感；在群体层面，定价差异可能导致对性别、种族等受保护群体的歧视。现有公平定价研究通常分别处理个体与群体公平性问题。本文通过提出一种融合双重公平性维度的社交网络个性化定价新模型，填补了该研究空白。为解决该问题，我们提出FairPricing——一种基于图神经网络（GNN）的新型框架，利用顾客特征与网络拓扑结构学习个性化定价策略。在FairPricing中，个体感知不公平性通过顾客需求惩罚项（进而影响利润目标）予以刻画，而群体层面歧视则通过对抗性去偏和价格正则化项进行缓解。与现有基于优化的个性化定价方法（需在网络更新时重新优化）不同，FairPricing习得的定价策略能根据顾客特征及新网络结构，为更新后网络中所有顾客分配个性化价格，从而实现对网络变化的泛化能力。大量实验结果表明，FairPricing在提升个体公平感知、满足群体公平要求的同时，实现了高盈利性。