  In real-world recommender systems, implicitly collected user feedback, while
abundant, often includes noisy false-positive and false-negative interactions.
The possible misinterpretations of the user-item interactions pose a
significant challenge for traditional graph neural recommenders. These
approaches aggregate the users' or items' neighbours based on implicit
user-item interactions in order to accurately capture the users' profiles. To
account for and model possible noise in the users' interactions in graph neural
recommenders, we propose a novel Diffusion Graph Transformer (DiffGT) model for
top-k recommendation. Our DiffGT model employs a diffusion process, which
includes a forward phase for gradually introducing noise to implicit
interactions, followed by a reverse process to iteratively refine the
representations of the users' hidden preferences (i.e., a denoising process).
In our proposed approach, given the inherent anisotropic structure observed in
the user-item interaction graph, we specifically use anisotropic and
directional Gaussian noises in the forward diffusion process. Our approach
differs from the sole use of isotropic Gaussian noises in existing diffusion
models. In the reverse diffusion process, to reverse the effect of noise added
earlier and recover the true users' preferences, we integrate a graph
transformer architecture with a linear attention module to denoise the noisy
user/item embeddings in an effective and efficient manner. In addition, such a
reverse diffusion process is further guided by personalised information (e.g.,
interacted items) to enable the accurate estimation of the users' preferences
on items. Our extensive experiments conclusively demonstrate the superiority of
our proposed graph diffusion model over ten existing state-of-the-art
approaches across three benchmark datasets.
