  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.
