Recovering masked feedback with neural models is a popular paradigm in recommender systems. Seeing the success of diffusion models in solving ill-posed inverse problems, we introduce a conditional diffusion framework for collaborative filtering that iteratively reconstructs a user's hidden preferences guided by its historical interactions. To better align with the intrinsic characteristics of implicit feedback data, we implement forward diffusion by applying synthetic smoothing filters to interaction signals on an item-item graph. The resulting reverse diffusion can be interpreted as a personalized process that gradually refines preference scores. Through graph Fourier transform, we equivalently characterize this model as an anisotropic Gaussian diffusion in the graph spectral domain, establishing both forward and reverse formulations. Our model outperforms state-of-the-art methods by a large margin on one dataset and yields competitive results on the others.

翻译：利用神经模型恢复掩码反馈是推荐系统中的一种流行范式。鉴于扩散模型在解决不适定逆问题方面的成功，我们引入了一种条件扩散框架用于协同过滤，该框架通过用户的历史交互逐步迭代地重建用户的隐含偏好。为了更好地适配隐式反馈数据的内在特性，我们在项目-项目图上对交互信号应用合成平滑滤波器来实现前向扩散。由此产生的反向扩散可以解释为一个逐步细化偏好得分的个性化过程。通过图傅里叶变换，我们等价地将该模型表征为图谱域中的各向异性高斯扩散，并建立了前向和反向公式。我们的模型在一个数据集上以较大优势超越了现有最先进方法，并在其他数据集上取得了具有竞争力的结果。