关键词: 推荐系统, 多模态, 对比学习, 语义融合, 特征解耦

Abstract: Multimodal recommendation aims to enhance user and item feature representations by integrating multimodal information to improve recommendation performance. Existing methods still face challenges including insufficient cross-modal semantic information fusion, redundant multimodal features, and noise interference. To address these issues, a Semantic Fusion and Contrastive Enhancement-based multimodal recommendation method was proposed. First, a cross-modal semantic consistency enhancement framework was designed, which constructed a global correlation graph through a multimodal semantic feature filtering mechanism to dynamically aggregate common multimodal features while suppressing noise propagation. Concurrently, a multi-granularity attribute disentanglement module was introduced to separate coarse-grained common features from user behavior-driven fine-grained features within modal representations, to mitigate feature redundancy. Second, a multi-level contrastive learning paradigm was proposed, integrating four tasks: cross-modal consistency alignment, user behavior similarity modeling, item semantic relevance constraint, and explicit-latent feature mutual information maximization. This framework enhances representation discriminability through contrastive learning. Furthermore, a graph perturbation enhancement strategy was incorporated, employing noise injection and dual contrastive regularization to improve model robustness against sparse data and noise interference. Experiments on Amazon-Baby, Amazon-Sports, and Amazon-Clothing datasets demonstrate that this method outperforms all baseline models in both R@20 and N@20 metrics, with particularly superior performance in sparse scenarios. Ablation studies further validate the effectiveness of the proposed method.

Key words: recommender system, multimodal, contrastive learning, semantic fusion, feature disentanglement