Personalized Pricing in Social Networks with Individual and Group Fairness Considerations

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.

💡 Research Summary

The paper tackles the emerging challenge of designing personalized pricing strategies in social network environments while simultaneously addressing two distinct fairness concerns: individual‑level perceived unfairness and group‑level discrimination against protected attributes such as gender or race. Traditional approaches treat these dimensions separately—either bounding price differences across neighboring users or imposing statistical parity constraints on price distributions across demographic groups. This separation limits practical deployment, especially when the underlying network evolves (new users join, friendships change).

To bridge this gap, the authors formulate a unified optimization problem. Customers are represented as nodes in a graph G = (V, E, X, S), where X denotes non‑protected features and S a binary protected attribute. Each node i has a willingness‑to‑pay distribution F_{˜x_i} known to the retailer. The expected demand under price p_i is d_i(p_i)=1−F_{˜x_i}(p_i), and profit π_i(p_i)=(p_i−c)·d_i(p_i). Individual unfairness is quantified by the price gap Δ_i between a node’s price and the average price of its neighbors. This gap is passed through an asymmetric hyperbolic‑tangent mapping η_i(p) that captures loss‑aversion (α < β) and yields a bounded perception score in