Wavelet-Enhanced Guidance for Diffusion-Based Sequential Recommendation
ZHOU Xi1, XIA Hongbin1,2, WANG Xiaofeng1,3
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2. Jiangsu Key University Laboratory of Software and Media Tech-nology under Human-Computer Cooperation, Jiangnan Univer-sity, Wuxi 214122; 3. Pengcheng Laboratory, Shenzhen 518000
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.
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2025, Vol. 38 
