Generative methods have gained widespread attention in Collaborative Filtering (CF) tasks for their ability to produce high-quality personalized samples aligned with users' interests. Among them, diffusion generative models have raised increasing attention in recommendation field. Despite that the pioneering efforts have applied the conventional diffusion process to model diffusive user interests, the incongruity between the Gaussian noise and the subtle nature of user's personalized interaction behavior has led to sub-optimal results. To this end, we introduce a specifically-tailored diffusion scheme for interaction systems, namely the interests burn-down process. The interests burn-down process delineates the decay of user interests towards candidate items, complemented by its reverse burn-up process that yields personalized recommendation for users. The inherent burn-down nature of this process adeptly models the diffusive user interests, aligning seamlessly with the requirements of CF tasks. We present a novel recommendation method StageCF to illustrate the superiority of this newly proposed diffusion process. Experimental results have demonstrated the effectiveness of StageCF against existing generative and diffusion-based baseline methods. Furthermore, comprehensive studies validate the functionality of interests burn-down process, shedding light on its capacity to generate personalized interactions.

翻译：生成式方法因其能生成符合用户兴趣的高质量个性化样本，在协同过滤任务中受到广泛关注。其中，扩散生成模型在推荐领域引起了越来越多的关注。尽管已有开创性研究将传统扩散过程应用于建模用户兴趣的扩散现象，但高斯噪声与用户个性化交互行为的微妙本质之间的不匹配导致结果次优。为此，我们提出一种专为交互系统定制的扩散方案——兴趣消解过程。该过程描述了用户对候选物品兴趣的衰减现象，并通过其逆过程（兴趣重建过程）为用户生成个性化推荐。该过程固有的消解特性能够精准建模用户兴趣的扩散现象，与协同过滤任务的需求完美契合。我们提出新型推荐方法StageCF，以验证这一新扩散方案的优越性。实验结果表明，StageCF相较于现有生成式及基于扩散的基线方法具有显著有效性。此外，综合实验验证了兴趣消解过程的功能性，揭示了其在生成个性化交互行为方面的潜力。