Micro-video recommendation aims to capture user preferences from the collaborative and context information of the interacted micro-videos, thereby predicting the appropriate videos. This target is often hindered by the inherent noise within multimodal content and unreliable implicit feedback, which weakens the correspondence between behaviors and underlying interests. While conventional works have predominantly approached such scenario through behavior-augmented modeling and content-centric multimodal analysis, these paradigms can inadvertently give rise to two non-trivial challenges: preference-irrelative video representation extraction and inherent modality conflicts. To address these issues, we propose a Multi-granularity sequential modeling method via hierarchical diffusion models for micro-video Recommendation (MealRec), which simultaneously considers temporal correlations during preference modeling from intra- and inter-video perspectives. Specifically, we first propose Temporal-guided Content Diffusion (TCD) to refine video representations under intra-video temporal guidance and personalized collaborative signals to emphasize salient content while suppressing redundancy. To achieve the semantically coherent preference modeling, we further design the Noise-unconditional Preference Denoising (NPD) to recovers informative user preferences from corrupted states under the blind denoising. Extensive experiments and analyses on four micro-video datasets from two platforms demonstrate the effectiveness, universality, and robustness of our MealRec, further uncovering the effective mechanism of our proposed TCD and NPD. The source code and corresponding dataset will be available upon acceptance.

翻译：微视频推荐旨在从交互微视频的协同信息与上下文信息中捕捉用户偏好，从而预测合适的视频。这一目标常受到多模态内容固有噪声及不可靠隐式反馈的阻碍，削弱了行为与潜在兴趣间的对应关系。传统研究主要通过行为增强建模和以内容为中心的多模态分析来处理此类场景，但这些范式可能无意中引发两个关键挑战：偏好无关的视频表征提取和固有的模态冲突。为解决这些问题，我们提出一种基于层次扩散模型的微视频多粒度序列建模推荐方法（MealRec），该方法在偏好建模过程中同时从视频内和视频间视角考虑时序相关性。具体而言，我们首先提出时序引导内容扩散（TCD），在视频内时序引导和个性化协同信号下优化视频表征，以突出显著内容并抑制冗余。为实现语义连贯的偏好建模，我们进一步设计噪声无条件偏好去噪（NPD），通过盲去噪从受损状态中恢复信息丰富的用户偏好。在两个平台的四个微视频数据集上进行的大量实验与分析证明了MealRec的有效性、普适性和鲁棒性，并进一步揭示了所提TCD与NPD模块的作用机制。源代码及相关数据集将在论文录用后公开。