A Structure-Aware Fair Recommendation Approach Based on Counterfactual Dynamic Hypergraphs

Unfair recommendations stem from user-sensitive attributes and information transmission biases. Graph-structured data can provide more balanced information for fair recommendations by capturing multidimensional user–item interactions. However, graph-based fair recommendation still faces some challenges: Traditional graphs rely on static edge-connected topology, struggling to dynamically update many-to-many relationships, which impairs the long-term fairness modeling; Most existing graph mining algorithms overlook individual differences arising from filtered sensitive information, thereby exacerbating the fairness-accuracy tradeoff; Hypergraph neural networks’ propagation relies on structural density, while sparse connections reduce it, leading to inaccurate representations in sparse regions and uneven diffusion. To address these issues, we propose a structure-aware fair recommendation approach based on counterfactual dynamic hypergraphs (FairCH). First, we propose a multidimensional user fairness model that captures many-to-many higher-order user–item relationships and their preference-fairness co-evolution via dynamic hypergraphs. Second, sensitive information is filtered through adversarial learning, and counterfactual hyperedges is reconstructed by counterfactual reasoning, compensating for information loss. Finally, a cross-hierarchy structure-aware model is proposed, which extracts counterfactual fairness layers, global preference layers, and shared evolution layers from hypergraphs and integrates them via an inter-layer interactive attention mechanism to enhance information propagation and mitigate structural biases. Experimental results demonstrate that FairCH exhibits superior recommendation performance to the baselines.