MURAL：基于自适应边学习的多模态不确定性感知推荐

AI 摘要

MURAL 是一个针对多模态推荐的统一框架，通过自适应边学习解决现有图神经网络的静态结构和语义脆弱性问题。它采用可微检索增强策略发现潜在物品关联，并利用不确定性感知融合模块动态降低噪声模态权重。实验表明，在 TikTok 和 Amazon 等基准上，MURAL 显著优于现有方法，并具备可解释性和鲁棒性。

正文节选

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning Abstract Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two bottlenecks: structural rigidity, from a reliance on static precomputed similarity graphs that cannot adapt to evolving preferences; and semantic fragility, where noisy modality signals are indiscriminately fused, distorting the collaborative s

发布时间：2026-09-07 12:00

抓取时间：2026-09-07 12:30

来源机构：arXiv