The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

翻译：多模态推荐已逐渐成为在线媒体平台的基础设施，通过联合建模用户历史行为（如购买、点击）与物品多模态特征（如视觉与文本特征），为用户提供个性化服务。现有研究大多聚焦于利用模态特征或模态相关图结构学习用户的局部兴趣。然而，这些方法存在两个局限性：（1）用户ID嵌入的共享更新导致协同信号与多模态信号产生耦合；（2）缺乏对鲁棒全局用户兴趣的探索，难以缓解局部兴趣建模面临的交互稀疏问题。针对上述问题，我们提出了一种新型的局部与全局图学习引导的多模态推荐模型（LGMRec），该模型能联合建模用户的局部与全局兴趣。具体而言，我们设计了局部图嵌入模块，通过局部拓扑关系独立学习用户和物品的协同相关嵌入与模态相关嵌入。此外，还构建了全局超图嵌入模块，通过建模具有洞察力的全局依赖关系来捕获全局用户与物品嵌入。在超图嵌入空间获取的全局嵌入可与两个解耦的局部嵌入相结合，从而提升推荐的准确性与鲁棒性。在三个基准数据集上的广泛实验表明，我们的LGMRec在多个最先进的推荐基线方法中展现出优越性，验证了其在建模局部与全局用户兴趣方面的有效性。