Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential. However, existing studies on diffusion model lack effective solutions for modeling implicit feedback. Particularly, the standard isotropic diffusion process overlooks correlation between items, misaligned with the graphical structure of the interaction space. Meanwhile, Gaussian noise destroys personalized information in a user's interaction vector, causing difficulty in its reconstruction. In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF). To better represent the correlated distribution of user-item interactions, we define a generalized diffusion process using heat equation on the item-item similarity graph. Our forward process smooths interaction signals with an advanced family of graph filters, introducing the graph adjacency as beneficial prior knowledge for recommendation. Our reverse process iteratively refines and sharpens latent signals in a noise-free manner, where the updates are conditioned on the user's history and computed from a carefully designed two-stage denoiser, leading to high-quality reconstruction. Finally, through extensive experiments, we show that GiffCF effectively leverages the advantages of both diffusion model and graph signal processing, and achieves state-of-the-art performance on three benchmark datasets.

翻译：协同过滤是推荐系统中的关键技术，已被越来越多地视为用户反馈数据的条件生成任务，其中新发展的扩散模型展现出巨大潜力。然而，现有扩散模型研究在建模隐式反馈方面缺乏有效解决方案。具体而言，标准各向同性扩散过程忽略了项目间的相关性，与交互空间的图结构不一致。同时，高斯噪声会破坏用户交互向量中的个性化信息，导致重建困难。本文对标准扩散模型进行改进，提出一种新颖的图信号扩散模型用于协同过滤（名为GiffCF）。为更好表示用户-项目交互的相关分布，我们利用项目-项目相似度图上的热方程定义了广义扩散过程。前向过程通过先进的图滤波器族平滑交互信号，将图邻接关系作为有益的先验知识引入推荐。反向过程以无噪声的方式迭代精炼与锐化潜在信号，更新过程以用户历史为条件并通过精心设计的两阶段去噪器计算，从而实现高质量重建。最后，通过大量实验证明，GiffCF有效结合了扩散模型与图信号处理的双重优势，在三个基准数据集上达到最先进性能。