Multimodal Recommendation (MMR) systems are crucial for modern platforms but are often hampered by inherent noise and uncertainty in modal features, such as blurry images, diverse visual appearances, or ambiguous text. Existing methods often overlook this modality-specific uncertainty, leading to ineffective feature fusion. Furthermore, they fail to leverage rich similarity patterns among users and items to refine representations and their corresponding uncertainty estimates. To address these challenges, we propose a novel framework, Similarity Propagation-enhanced Uncertainty for Multimodal Recommendation (SPUMR). SPUMR explicitly models and mitigates uncertainty by first constructing the Modality Similarity Graph and the Collaborative Similarity Graph to refine representations from both content and behavioral perspectives. The Uncertainty-aware Preference Aggregation module then adaptively fuses the refined multimodal features, assigning greater weight to more reliable modalities. Extensive experiments on three benchmark datasets demonstrate that SPUMR achieves significant improvements over existing leading methods.

翻译：多模态推荐系统在现代平台中至关重要，但常受模态特征固有噪声和不确定性的阻碍，例如模糊图像、多样视觉外观或模糊文本。现有方法常忽视这种模态特定不确定性，导致特征融合效果不佳。此外，它们未能利用用户和物品间丰富的相似性模式来优化表征及其相应不确定性估计。为解决这些挑战，我们提出一种新颖框架——基于相似性传播增强不确定性的多模态推荐系统。该框架通过首先构建模态相似性图与协同相似性图，从内容与行为双重视角优化表征，从而显式建模并缓解不确定性。随后，不确定性感知偏好聚合模块自适应地融合优化后的多模态特征，为更可靠的模态分配更高权重。在三个基准数据集上的大量实验表明，SPUMR相较于现有领先方法实现了显著性能提升。