MURAL：基于自适应边学习的多模态不确定性感知推荐
MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning
- American University（美利坚大学）
- Ulsan National Institute of Science and Technology（蔚山国立科学技术院）
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
MURAL是基于自适应边学习的多模态推荐框架，通过动态拓扑发现与不确定性感知融合等技术，在TikTok、Amazon等基准上显著超越现有方法，兼具高准确率、可解释性与鲁棒性。
AI中文摘要：
多模态图神经网络已成为推荐系统的标准方法，其通过内容特征扩充稀疏的交互数据。然而当前架构面临两大瓶颈：一是结构刚性，依赖静态预计算的相似性图，无法适配不断演变的偏好；二是语义脆弱性，会不加区分地融合带噪声的模态信号，从而扭曲协作信号。本文提出MURAL（Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning），这是一个统一框架，将多模态推荐从固定结构扩充转向动态拓扑发现。为解决结构刚性问题，自适应边学习器结合可微检索增强策略与近似最近邻搜索，以发现语义自适应且计算可扩展（时间复杂度为O(NlogN)）的潜在物品-物品关联。为解决语义脆弱性问题，不确定性感知融合模块对异构模态的随机不确定性进行建模，动态降低不可靠特征的权重，同时优先考虑高置信度信号以抵御跨模态噪声。本文进一步采用对比师生对齐方法，将模态特定表示锚定到稳定的行为信号，确保优化稳定性且无梯度泄漏。在TikTok和Amazon等大规模基准数据集上的实验表明，MURAL显著超越了结构类和生成类的基线方法，在实现更优准确率的同时，可通过特定领域的模态主导性提供可解释性，并在极端数据损坏下具备鲁棒性。
英文摘要：
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 signal. We propose MURAL (Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning), a unified framework that shifts multimodal recommendation from fixed structural augmentation to dynamic topology discovery. To address structural rigidity, an Adaptive Edge Learner combines a differentiable retrieval-augmented strategy with an approximate nearest neighbor search to discover latent item-item correlations that are both semantically adaptive and computationally scalable (O(NlogN)). To address semantic fragility, an Uncertainty-Aware Fusion module models the aleatoric uncertainty of heterogeneous modalities, dynamically down-weighting unreliable features while prioritizing high-confidence signals as a defense against cross-modal noise. We further employ a contrastive teacher-student alignment that anchors modality-specific representations to stable behavioral signals, ensuring optimization stability without gradient leakage. Experiments on large-scale benchmarks including TikTok and Amazon show that MURAL significantly surpasses both structural and generative state-of-the-art baselines, achieving superior accuracy while offering interpretability through domain-specific modality dominance and robustness under extreme data corruption.
