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.

翻译：在现实世界的推荐系统中，隐式收集的用户反馈虽然丰富，但通常包含带有噪声的假阳性和假阴性交互。这种对用户-物品交互可能存在的错误解读，给传统图神经网络推荐器带来了重大挑战。这些方法基于隐式用户-物品交互聚合用户或物品的邻居，以准确捕捉用户画像。为了在神经网络推荐器中考虑并建模用户交互中可能存在的噪声，我们提出了一种新颖的扩散图Transformer（DiffGT）模型，用于Top-K推荐。我们的DiffGT模型采用扩散过程，包括一个前向阶段逐步向隐式交互引入噪声，随后是一个反向过程迭代细化用户隐藏偏好的表示（即去噪过程）。在所提出的方法中，鉴于用户-物品交互图中固有的各向异性结构，我们在前向扩散过程中专门使用了各向异性且具有方向性的高斯噪声。我们的方法与现有扩散模型中仅使用各向同性高斯噪声的做法不同。在反向扩散过程中，为了逆转先前添加的噪声影响并恢复用户的真实偏好，我们整合了一种带有线性注意力模块的图Transformer架构，以高效且有效的方式对含噪声的用户/物品嵌入进行去噪。此外，这种反向扩散过程还受到个性化信息（如交互过的物品）的引导，从而能够准确估计用户对物品的偏好。我们的大量实验最终证明了所提出的图扩散模型在三个基准数据集上优于十个现有最先进方法的卓越性能。