Graph Neural Networks with Shapley-Value Explanations for Hierarchical Recommendation Systems

Hierarchical structures are prevalent in real-world recommendation systems; however, existing graph neural networks (GNNs) struggle to capture them effectively because of their reliance on Euclidean geometry and a lack of Interpretability. This paper presents a novel architecture, Hyperbolic Graph Neural Networks with Shapley-Value Explanations (HGNN-SV), which simultaneously addresses both challenges in hierarchical recommendation tasks. Our method combines Poincar´e ball hyperbolic embeddings with Shapley-value-based feature attributions, enabling accurate modelling of tree-like user–item relationships while offering transparent, theoretically grounded explanations for each recommendation. Experiments on the Amazon Product Reviews and MovieLens 1M datasets demonstrated strong performance across multiple evaluation metrics. On MovieLens-1M, HGNN-SV achieved a Precision@10 of 0.822, Recall@10 of 0.785, and F1-Score@10 of 0.803. For Amazon Product Reviews, the method attained a Precision@10 of 0.785, Recall@10 of 0.730, and F1-Score@10 of 0.756. A comparative evaluation against leading baselines, including LightGCN, Hyperbolic GCN, GNNShap, and MAGE, shows that our unified approach consistently outperforms existing methods across all metrics. Moreover, the generated Shapley attribution closely aligned with semantic item hierarchies, as validated through systematic evaluation. By bridging the gap between geometric expressiveness and interpretability, our approach establishes a new benchmark for trustworthy, high-fidelity hierarchical recommendation systems.