Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the noise issues from data cleaning perspective such as data resampling and reweighting, but they are constrained by heuristic assumptions. Another denoising avenue is from model perspective, which proactively injects noises into user-item interactions and enhance the intrinsic denoising ability of models. However, this kind of denoising process poses significant challenges to the recommender model's representation capacity to capture noise patterns. To address this issue, we propose Denoising Diffusion Recommender Model (DDRM), which leverages multi-step denoising process based on diffusion models to robustify user and item embeddings from any recommender models. DDRM injects controlled Gaussian noises in the forward process and iteratively removes noises in the reverse denoising process, thereby improving embedding robustness against noisy feedback. To achieve this target, the key lies in offering appropriate guidance to steer the reverse denoising process and providing a proper starting point to start the forward-reverse process during inference. In particular, we propose a dedicated denoising module that encodes collaborative information as denoising guidance. Besides, in the inference stage, DDRM utilizes the average embeddings of users' historically liked items as the starting point rather than using pure noise since pure noise lacks personalization, which increases the difficulty of the denoising process.

翻译：推荐系统常常面临噪声隐式反馈的困扰。大多数研究从数据清洗角度缓解噪声问题，例如数据重采样和重新加权，但这些方法受限于启发式假设。另一种去噪途径是从模型角度出发，主动将噪声注入用户-物品交互中，并增强模型内在的去噪能力。然而，这类去噪过程对推荐模型捕捉噪声模式的表征能力提出了重大挑战。为解决这一问题，我们提出了去噪扩散推荐模型（DDRM），该模型基于扩散模型采用多步去噪过程，从而增强任意推荐模型中用户和物品嵌入的鲁棒性。DDRM在前向过程中注入受控的高斯噪声，并在反向去噪过程中迭代去除噪声，从而提升嵌入对噪声反馈的鲁棒性。为实现这一目标，关键在于提供适当的指导来引导反向去噪过程，并在推理阶段为前向-反向过程提供合适的起点。具体而言，我们设计了一个专门去噪模块，将协同信息编码为去噪指导。此外，在推理阶段，DDRM利用用户历史喜欢物品的平均嵌入作为起点，而非纯噪声，因为纯噪声缺乏个性化，会增加去噪过程的难度。