Conditional Gaussian Mixture Denoising for Cross-Domain Sequential Recommendation

Cross-domain sequential recommendation is widely studied for alleviating the cold-start problem via knowledge transfer. Existing methods usually derive the user's noisy domain-shared multi-interest (NDI) from multi-domain interactions, with diffusion as an effective denoising strategy for adapting NDI to the target domain. However, this diffusion denoising process is initialized with a fixed, user-agnostic Gaussian prior, which may be far from the user's NDI, resulting in an excessively long denoising path. To address this, we propose a personalized prior initialization strategy, which enables efficient user adaptation by learning a user-specific NDI distribution as the denoising starting point. Specifically, a domain-shared preference modeling strategy employs a codebook-based Gaussian mixture distribution to capture the user's NDI, with a pairwise proximity regularizer preventing the mixture components from being excessively separated. Building on this, a target-domain adaptation mechanism reformulates the noising and denoising processes under the learned NDI prior, with a domain-specific log-density condition steering the denoising trajectory toward the target domain. Experiments on three real-world datasets show that our model converges in substantially fewer denoising steps than a variant using the standard Gaussian prior, without relying on step-skipping samplers, while also improving recommendation accuracy.