发表机构
University of Amsterdam; University of Hong Kong; Aarhus University（阿姆斯特丹大学; 香港大学; 奥胡斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TimeRoute通过时间感知模态路由器和带FiLM的双流去噪扩散图重构器，解决多模态推荐的模态时间尺度不匹配问题，在三个公开数据集上提升了推荐指标。
AI 中文摘要
多模态推荐系统将协同信号与文本、图像、音频等物品模态相融合，但各模态的有用性会随时间推移以不同速率发生变化。例如，巧克力购买通常由文本成分线索引导，但在情人节前后会转向视觉包装和环境音频。这种模态时间尺度不匹配引发两个耦合挑战：一是用户在不同时间情境下需要不同的模态比例；二是相关性较低的模态更易向推荐系统引入过时或误导性信号。我们在统一的基于扩散的推荐模型TimeRoute中解决这两个挑战：时间感知模态路由器将每个用户的聚合行为特征映射到个性化模态分布，替代了先前工作中全局共享的融合权重；基于扩散的图重构器随后通过特征-wise线性调制（FiLM），利用双流长短期去噪头，以相同时间分布为条件，在过时模态边进入传播图前将其抑制。在TikTok、Amazon-Baby和Amazon-Sports数据集上的实验表明，在10次种子配对测试中，该模型相较于强基线方法在Recall@K、Precision@K和NDCG@K指标上实现了最高达9.8%的持续提升。代码可在该https URL获取。
英文摘要
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates. For example, around Valentine's Day, chocolate purchases become less driven by textual ingredient cues and more by visual packaging and ambient audio. This \emph{modality time-scale mismatch} gives rise to two coupled challenges: (1) users with different temporal behavior profiles require different modality proportions, and (2) less relevant modalities are more likely to introduce outdated or misleading signals into the recommender. We address both challenges within a unified diffusion-based recommender, \textbf{TimeRoute}. A temporal-aware modal router maps each user's aggregated temporal profile to a personalized modality distribution, replacing the globally shared fusion weights used in prior work. The diffusion-based graph reconstructor is conditioned on the same profile through Feature-wise Linear Modulation (FiLM) with dual-stream long- and short-term denoising heads. This design captures both slowly and rapidly evolving temporal dynamics to suppress outdated modality edges before they enter the propagation graph. Experiments on TikTok, Amazon-Baby, and Amazon-Sports, averaged over 10 seeds, demonstrate consistent improvements over strong baselines across Recall@K, Precision@K, and NDCG@K, reaching up to 9.8\% (P@20 on Amazon-Baby). Controlled attribution studies further show that these gains require both the proposed mechanisms and temporal input: naively granting the backbone the same temporal profile yields no benefit, and feeding the router random noise performs no better than removing the router entirely. Code is available at https://anonymous.4open.science/r/TimeRoute.
