Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accurate modeling of the complex user interaction generation procedure, such as noisy interactions caused by various interference factors. In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. To retain personalized information in user interactions, DiffRec reduces the added noises and avoids corrupting users' interactions into pure noises like in image synthesis. In addition, we extend traditional DMs to tackle the unique challenges in practical recommender systems: high resource costs for large-scale item prediction and temporal shifts of user preference. To this end, we propose two extensions of DiffRec: L-DiffRec clusters items for dimension compression and conducts the diffusion processes in the latent space; and T-DiffRec reweights user interactions based on the interaction timestamps to encode temporal information. We conduct extensive experiments on three datasets under multiple settings (e.g. clean training, noisy training, and temporal training). The empirical results and in-depth analysis validate the superiority of DiffRec with two extensions over competitive baselines.

翻译：生成对抗网络（GANs）和变分自编码器（VAEs）等生成模型被广泛用于建模用户交互的生成过程。然而，这些生成模型存在固有局限性，如GANs的不稳定性和VAEs表征能力受限。这些局限阻碍了对复杂用户交互生成过程的精确建模，例如由多种干扰因素导致的噪声交互。鉴于扩散模型（DMs）在图像合成领域相较传统生成模型具有显著优势，我们提出新型扩散推荐模型（命名为DiffRec），以去噪方式学习生成过程。为了保留用户交互中的个性化信息，DiffRec降低添加噪声的强度，避免像图像合成那样将用户交互完全退化为纯噪声。此外，我们扩展传统DMs以应对实际推荐系统中的独特挑战：大规模物品预测的高资源消耗以及用户偏好的时间漂移。为此，我们提出DiffRec的两种扩展：L-DiffRec通过对物品聚类实现维度压缩，并在潜在空间进行扩散过程；T-DiffRec基于交互时间戳重新加权用户交互以编码时间信息。我们在三个数据集上进行了多场景（如干净训练、噪声训练和时间训练）的广泛实验。实证结果和深入分析验证了包含两种扩展的DiffRec相较于竞争基线方法的优越性。