In the realm of recommender systems, handling noisy implicit feedback is a prevalent challenge. While most research efforts focus on mitigating noise through data cleaning methods like resampling and reweighting, these approaches often rely on heuristic assumptions. Alternatively, model perspective denoising strategies actively incorporate noise into user-item interactions, aiming to bolster the model's inherent denoising capabilities. Nonetheless, this type of denoising method presents substantial challenges to the capacity of the recommender model to accurately identify and represent noise patterns. To overcome these hurdles, we introduce a plug-in diffusion model for embedding denoising in recommendation system, which employs a multi-step denoising approach based on diffusion models to foster robust representation learning of embeddings. Our model operates by introducing controlled Gaussian noise into user and item embeddings derived from various recommender systems during the forward phase. Subsequently, it iteratively eliminates this noise in the reverse denoising phase, thereby augmenting the embeddings' resilience to noisy feedback. The primary challenge in this process is determining direction and an optimal starting point for the denoising process. To address this, we incorporate a specialized denoising module that utilizes collaborative data as a guide for the denoising process. Furthermore, during the inference phase, we employ the average of item embeddings previously favored by users as the starting point to facilitate ideal item generation. Our thorough evaluations across three datasets and in conjunction with three classic backend models confirm its superior performance.

翻译：在推荐系统领域，处理含噪声的隐式反馈是一个普遍存在的挑战。尽管大多数研究通过重采样和重加权等数据清理方法致力于减轻噪声影响，但这些方法往往依赖启发式假设。另一种基于模型视角的去噪策略则主动将噪声融入用户-物品交互中，旨在增强模型固有的去噪能力。然而，此类去噪方法对推荐模型准确识别和表征噪声模式的能力提出了重大挑战。为克服这些障碍，我们提出了一种用于推荐系统嵌入去噪的即插即用扩散模型，该模型采用基于扩散模型的多步去噪方法，以促进嵌入的鲁棒表示学习。在前向阶段，我们的模型将受控高斯噪声引入来自不同推荐系统的用户和物品嵌入中；随后在反向去噪阶段迭代消除该噪声，从而增强嵌入对噪声反馈的稳健性。该过程中的主要挑战在于确定去噪的方向和最优起点。为此，我们引入了一个专用去噪模块，利用协同数据指导去噪过程。此外，在推理阶段，我们采用用户先前偏好的物品嵌入的平均值作为起点，以促进理想物品的生成。我们在三个数据集上结合三种经典后端模型进行的全面评估证实了其优越性能。