Multimodal sequential recommendation method based on time-frequency transformation and dynamic graph attention

Sui Xinyi; Wang Ruiqin; Ren Yubin; Fang Chi

doi:10.11959/j.issn.1000-0801.DXKX250483

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Multimodal sequential recommendation method based on time-frequency transformation and dynamic graph attention

Research and Development | 更新时间：2026-06-17

- Multimodal sequential recommendation method based on time-frequency transformation and dynamic graph attention
- Telecommunications Science Vol. 42, Issue 5, Pages: 169-182(2026)
- 作者机构：
  
  湖州师范学院信息工程学院，浙江湖州 313000
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX250483
  CLC： TP391.3;TN919.3
- Received：30 July 2025，
  
  Revised：2025-11-19，
  
  Accepted：19 November 2025，
  
  Published：20 May 2026
- 稿件说明：
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隋欣怡,王瑞琴,任宇彬等.基于时频变换与动态图注意力的多模态序列推荐方法[J].电信科学,2026,42(05):169-182.

Sui Xinyi,Wang Ruiqin,Ren Yubin,et al.Multimodal sequential recommendation method based on time-frequency transformation and dynamic graph attention[J].Telecommunications Science,2026,42(05):169-182.
隋欣怡,王瑞琴,任宇彬等.基于时频变换与动态图注意力的多模态序列推荐方法[J].电信科学,2026,42(05):169-182. DOI： 10.11959/j.issn.1000-0801.DXKX250483.

Sui Xinyi,Wang Ruiqin,Ren Yubin,et al.Multimodal sequential recommendation method based on time-frequency transformation and dynamic graph attention[J].Telecommunications Science,2026,42(05):169-182. DOI： 10.11959/j.issn.1000-0801.DXKX250483.

摘要

针对多模态序列推荐中用户兴趣演化建模、跨模态语义对齐及时频特征提取的不足，提出小波增强的动态图注意力推荐（wavelet-enhanced dynamic graph attention recommendation，Wave-DGARec）模型。该模型从时频变换、图建模与对比学习这3个维度进行创新设计：引入多尺度小波变换模块，对行为序列进行时频重构，捕捉非平稳偏好波动；构建用户-商品-图像三元动态图，利用图注意力机制实现结构化语义在不同模态间的高效传播；设计跨模态对比学习机制，引入可学习温度参数，提升语义对齐质量与样本判别能力。在Amazon 4个领域数据集的实验验证了Wave-DGARec的优越性。消融实验证实了小波模块与动态图建模的有效性，为多模态推荐系统提供了一种融合时频分析与结构建模的新范式。

Abstract

To address limitations in modeling user interest dynamics

cross-modal semantic alignment

and time-frequency feature extraction in multimodal sequential recommendation

wavelet-enhanced dynamic graph attention recommendation (Wave-DGARec) was proposed. This framework introduced innovations in three dimensions. A multi-scale wavelet transformation module was introduced to reconstruct behavioral sequences in the time-frequency domain

enabling the capture of non-stationary preference fluctuations. A user-item-image tripartite dynamic graph was constructed

wherein a graph attention mechanism is leveraged to enable efficient propagation of structured semantics across different modalities. A cross-modal contrastive learning strategy with a learnable temperature parameter was designed

enhancing both semantic alignment and sample discrimination. Experimental results on four Amazon domain datasets demonstrated the superiority of Wave-DGARec. Ablation studies further validated the effectiveness of both the wavelet module and the dynamic graph modeling. This work introduced a novel paradigm for multimodal recommendation systems by seamlessly integrating time-frequency analysis with structured representation learning.

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references

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