Multimodal recommendation systems aim to model users and items by jointly leveraging multimodal content and collaborative signals, yet their performance is fundamentally constrained by data sparsity. To address this, recent studies have introduced diffusion models to generate additional structural information and enrich user-item representations. However, most remain limited to local interactions between users and items, failing to capture global relationships. We propose a novel modality-aware hypergraph edge diffusion recommendation model (MHRec), which integrates the high-order relational modeling capability of hypergraphs with the generative power of diffusion models to dynamically refine graph structures and enhance multimodal representation learning. Specifically, MHRec constructs modality-aware hypergraphs that adaptively capture both local interactions and global relations, and employs a diffusion-based strategy to optimize these structures. Finally, a dual-channel representation learning module jointly models users and items across modalities for robust and comprehensive representations. Experiments on three public datasets—Beauty, Sports, and MicroLens—demonstrate that MHRec consistently outperforms existing baselines. Notably, MHRec improves Recall@10 by 6.05%, 3.82%, and 4.10% on these datasets, respectively, showing its strong capability to alleviate data sparsity and enhance recommendation performance.
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