Knowledge-enhanced Hyperbolic Attention Network for Trustworthy Sequential Recommendation

Sequential recommendation aims to predict users’ next actions by modeling temporal dependencies in interaction sequences. However, existing attention- or graph-based models often struggle to jointly capture hierarchical semantics, long-range dependencies, and multi-relational knowledge, leading to suboptimal performance in sparse and cold-start scenarios. In this paper, we propose KHARec, a Knowledge-Enhanced Hyperbolic Attention Network that unifies geometric representation learning and temporal modeling for trustworthy sequential recommendation. KHARec consists of a hyperbolic fine-grain module that encodes hierarchical semantics from knowledge graphs, a Transformer-based coarse-grain module that models global dependencies with an FFT-based filter, and a learnable gating mechanism for adaptive feature fusion. Extensive experiments on three benchmark datasets demonstrate that KHARec consistently outperforms state-of-the-art sequential and knowledge-aware baselines in terms of HR@K and NDCG@K, while maintaining near-linear computational complexity. Further analyses verify the model's robustness under sparsity and cold-start conditions and its superior capability in handling long-tail items. Overall, KHARec provides an interpretable and efficient framework for integrating hyperbolic reasoning and attention-based sequence modeling in large-scale recommendation environments.