基于小波变换增强引导的扩散序列推荐模型

doi:10.16451/j.cnki.issn1003-6059.202510003

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Abstract Since most conditional diffusion-based sequential recommendation models directly extract guiding signals from the historical interaction sequences of users, the generated signals are susceptible to noise and lack sufficient contextual information, thereby limiting the generation capability of the model. To address these issues, wavelet-enhanced guidance for diffusion-based sequential recommendation（WEG4Rec） is proposed in this paper. First, multi-frequency segmentation results of historical interaction embeddings are obtained via the wavelet transform. On this basis, adaptive dimensional projection and linear attention are introduced to generate multi-granularity interest embeddings. Second, the multi-granularity interest embeddings are employed to guide the reverse reconstruction process of the diffusion model. Finally, a multi-task strategy is adopted to jointly optimize the recommendation model during the training. Extensive experiments on four real-world datasets demonstrate the superior performance of WEG4Rec.

Key words： Sequential Recommendation Conditional Diffusion Model Wavelet Transform Linear Attention

Received: 17 July 2025

TP 391

Fund:National Natural Science Foundation of China（No.61972182）

Corresponding Authors: XIA Hongbin, Ph.D., professor. His research interests include personalized recommendation, natural language processing, and computer networks.

About author:: ZHOU Xi, Master student. His research interests include recommendation systems and deep learning. WANG Xiaofeng, Ph.D., professor. His research interests include computer networks.

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	ZHOU Xi
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Cite this article:

ZHOU Xi,XIA Hongbin,WANG Xiaofeng. Wavelet-Enhanced Guidance for Diffusion-Based Sequential Recommendation[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(10): 893-910.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202510003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I10/893

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