MealRec: Multi-granularity Sequential Modeling via Hierarchical Diffusion Models for Micro-Video Recommendation

2026-03-02

AI Summary

This work addresses the challenges in micro-video recommendation posed by multimodal content noise and unreliable implicit feedback, which often result in weak alignment between user behaviors and true interests, representation bias, and modality conflicts. To tackle these issues, the authors propose a hierarchical diffusion model that jointly captures user preferences through dual-granularity temporal modeling—both within and across videos. The approach incorporates two key mechanisms: Temporal-guided Content Diffusion (TCD) for refining multimodal content and Noise-free Preference Denoising (NPD) for robustly recovering user preferences. Extensive experiments on four micro-video datasets demonstrate that the proposed method significantly outperforms state-of-the-art recommendation models, exhibiting strong effectiveness, generalizability, and robustness.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• micro-video recommendation
• multimodal noise
• implicit feedback
• preference-irrelative representation
• modality conflict

Innovation

• Hierarchical Diffusion Models
• Temporal-guided Content Diffusion
• Noise-unconditional Preference Denoising
• Multi-granularity Sequential Modeling
• Micro-video Recommendation