Graph neural networks (GNNs) have been widely applied to various recommendation scenarios due to their outstanding performance in capturing and modelling hidden relationships. However, as the performance of a GNN heavily relies on the graph structure, the structure bias can lead to significant unfairness problems in a GNN-based recommender system across different user groups. Furthermore, prior studies have shown that sensitive attributes of nodes can be revealed through the graph structure. In other words, even if a GNN-based recommendation system does not explicitly disclose a user’s sensitive information, it can still be inferred from the imbalance in users’ connections within the graph. This article therefore addresses the unfairness arising from such structural biases by proposing a novel model designed to mitigate these biases in GNN-based recommendation. Here, the fairness requirements refer to preventing the exposure of sensitive attributes during user modeling and avoiding biased recommendations across different demographic groups.
To address this issue, we propose a novel fairness-aware graph neural network model, EqStruct-GNN, which aims to mitigate inter-group unfairness at the structural level. The model is composed of three key modules: (1) the Adaptive Behavior Modeling Module, which computes fairness-aware node behavior probability distributions; (2) the Fairness-Aware Structure Construction Module, which builds graph structures based on these distributions and (3) the Fair Representation Learning Module, which generates unbiased node embeddings. Together, these modules enable EqStruct-GNN to construct a fairness-aware graph that reduces structural bias and improves fairness across sensitive groups.
Experimental results demonstrate that the proposed method achieves strong recommendation performance while ensuring fairness across multiple datasets. Furthermore, when applied to various backbone models, EqStruct-GNN not only significantly improves fairness metrics but also maintains or even enhances recommendation accuracy. These results highlight the generalization capability and practical effectiveness of the proposed framework.
Unlike existing methods that focus on representation-level fairness, our model tackles structural bias at its root by building a fairness-aware debiased graph through a novel graph construction mechanism. This structure enables the learning of fair and high-quality node representations by effectively mitigating structural bias while preserving critical interaction information. In addition, the proposed approach is universally adaptable and can be seamlessly applied to various GNN architectures, demonstrating strong generality and practical applicability.
