Personalized Pricing in Social Networks with Individual and Group Fairness Considerations

2025-12-11

AI Summary

This paper addresses the dual fairness challenges—individual fairness (user-perceived disparity) and group fairness (statistical discrimination)—arising from personalized pricing in social networks. Methodologically, we propose the first unified modeling framework that integrates graph neural networks (GNNs) to capture dynamic network structures, incorporates a demand-sensitive profit function to model user heterogeneity, and jointly enforces statistical parity and individual fairness via adversarial debiasing and structure-aware price regularization. Our key contribution lies in unifying demand-penalty-based individual fairness and adversarial learning-based group debiasing within a single pricing optimization objective, while enabling generalizable decisions under evolving network topologies. Experiments demonstrate that our framework maintains competitive platform revenue while significantly improving users’ fairness perception, strictly satisfying both individual and group fairness constraints, and exhibiting strong robustness to network updates.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Incorporates both individual and group fairness in personalized pricing within social networks.
• Proposes a GNN-based framework to learn pricing policies that adapt to network changes.
• Balances profitability with fairness by penalizing unfairness and mitigating group discrimination.

Innovation

• Graph neural networks learn personalized pricing policies
• Adversarial debiasing mitigates group-level discrimination
• Generalizes to network changes without re-optimization