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. Extensive experiments on three datasets with three representative backend recommender models demonstrate the effectiveness of DDRM.

翻译：推荐系统常面临噪声隐式反馈的困扰。现有研究多从数据清洗视角缓解噪声问题（如数据重采样与重加权），但受限于启发式假设。另一种去噪途径源于模型视角，即主动向用户-物品交互注入噪声以增强模型内在去噪能力。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严峻挑战。为此，我们提出去噪扩散推荐模型（DDRM），该模型基于扩散模型的多步去噪过程，可增强任意推荐模型中用户与物品嵌入的鲁棒性。DDRM在前向过程中注入受控高斯噪声，并在反向去噪过程中迭代消除噪声，从而提升嵌入对噪声反馈的鲁棒性。实现此目标的关键在于：为反向去噪过程提供恰当的引导方向，并在推理阶段为前向-反向过程设置合理的起点。具体而言，我们设计了专用去噪模块，将协同信息编码为去噪引导。此外，在推理阶段，DDRM采用用户历史喜爱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特征，会增大去噪过程的难度。在三个数据集上采用三种代表性后端推荐模型进行的广泛实验，验证了DDRM的有效性。