Cross-modal hyperedge alignment for knowledge graph augmented recommendation

Knowledge graph (KG)-enhanced recommender systems have been widely studied for alleviating data sparsity by incorporating rich relational semantics. However, existing KG-based models struggle to effectively model long-tail items due to the lack of reliable user-item supervision, which limits the exploitation of KG knowledge for recommendation. To address these challenges, we propose CHAKG, a cross-modal hyperedge alignment framework that compensates for missing supervision through efficient and selective interaction transfer. CHAKG projects both the KG and the user-item interaction graph into latent hyperedge spaces, enabling lightweight modeling of high-order semantic co-occurrence between interaction-rich items and long-tail items without expensive multi-hop propagation. Building on this representation, CHAKG introduces a consistency-guided interaction transfer mechanism, which selectively transfers reliable signals from interaction-rich items to long-tail items via a learnable denoising process. Furthermore, a cross-modal contrastive alignment objective enforces semantic consistency between knowledge and interaction views, improving robustness and generalization under sparse supervision. Extensive experiments on four benchmark datasets demonstrate that CHAKG achieves state-of-the-art accuracy and efficiency, particularly under cold-start and long-tail recommendation scenarios.