Structure-Activated and Interest-Aware Multimodal Recommendation Method

Multimodal recommendation systems improve recommendation performance by leveraging information from different modalities and complex relational networks. However, existing methods still face two main challenges: first, randomly initialized ID embeddings lack semantic and structural priors, making them vulnerable to optimization bias during training, which results in unstable representations and weakens their effectiveness as anchors for multimodal fusion. Second, GCNs typically adopt uniform aggregation for higher-order interactions, which oversmooths node representations and causes semantic degradation. To address these issues, the article proposes a novel multimodal recommendation method based on Structural Activation and Interest-Aware (SAIA). By introducing a perturbation mechanism to enhance the differentiation of ID embeddings, and incorporating gated fusion of modality features with dynamic graph convolution strategies, SAIA effectively solves the issues of homogeneous representations and the identification of node importance. Additionally, we utilize a semantic disentangling mechanism to achieve personalized modality fusion, and employ cross-space contrastive learning to further enhance the semantic compatibility and structural consistency of the fused modality representations. Experimental results show that SAIA outperforms existing methods on four benchmark datasets.