Recent Advances in Fairness Analysis of User Profiling Approaches in E-Commerce with Graph Neural Networks

User profiling is a critical procedure for e-commerce applications that captures online users’ attributes, understands user models, supports the provision of tailor-made goods and services, and improves user satisfaction. With the advent of novel technologies like Graph Neural Networks (GNNs), the performance of user profiling approaches has improved by leaps and bounds, in step with the growing concern about data and algorithmic fairness. This paper provides an overview of recent advances in the fairness analysis of GNN-based models for user profiling in the e-commerce domain. We present the results of our recent works addressing the need for an accurate analysis of state-of-the-art models and the lack of a unified tool for enabling any user to perform a fairness analysis on a specific dataset by leveraging the most performing models in this context. Our goal is to foster discussions on the potential implications of our work within the community, not only from a technical view but also from domain experts’ perspective.