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.

使用神经模型通过条件扩散框架来恢复掩盖的反馈，在隐式反馈数据上实现了前向扩散，利用图傅里叶变换在图谱域中等效地表征模型，并在一个数据集上大幅超越最先进的方法，并在其他数据集上获得有竞争力的结果。

面向协同过滤的图感知扩散建模

Towards Graph-Aware Diffusion Modeling for Collaborative Filtering

Domain-to-domain translation involves generating a target domain sample given
a condition in the source domain. Most existing methods focus on fixed input
and output domains, i.e. they only work for specific configurations (i.e. for
two domains, either $D_1\rightarrow{}D_2$ or $D_2\rightarrow{}D_1$). This paper
proposes Multi-Domain Diffusion (MDD), a conditional diffusion framework for
multi-domain translation in a semi-supervised context. Unlike previous methods,
MDD does not require defining input and output domains, allowing translation
between any partition of domains within a set (such as $(D_1,
D_2)\rightarrow{}D_3$, $D_2\rightarrow{}(D_1, D_3)$, $D_3\rightarrow{}D_1$,
etc. for 3 domains), without the need to train separate models for each domain
configuration. The key idea behind MDD is to leverage the noise formulation of
diffusion models by incorporating one noise level per domain, which allows
missing domains to be modeled with noise in a natural way. This transforms the
training task from a simple reconstruction task to a domain translation task,
where the model relies on less noisy domains to reconstruct more noisy domains.
We present results on a multi-domain (with more than two domains) synthetic
image translation dataset with challenging semantic domain inversion.

该研究论文介绍了一种新的多域翻译方法，称为 MDD（Multi-Domain Diffusion），它通过一种条件扩散框架在半监督环境中进行多域翻译，不需要定义输入和输出域，可以在给定的域集合内进行任意分区的翻译。