通过频谱域各向异性扩散，提升推荐系统中用户偏好恢复的准确性。
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain
- 在图谱频域中设计非各向同性扩散，保留低频重要信息
- 在多个数据集上优于基线模型，显著提升推荐性能
- 适合需要高精度偏好建模的推荐系统研究者
从用户-物品交互矩阵中恢复用户偏好是推荐系统的核心挑战。尽管扩散模型能从潜在分布中采样和重建偏好，但通常难以有效捕捉相似用户的集体偏好。此外，前向过程中潜在变量会退化为纯高斯噪声，导致信噪比下降，进而影响性能。为此，我们提出S-Diff，受基于图的协同过滤启发，更有效地利用图谱域中的低频分量。S-Diff将用户交互向量映射到谱域，并参数化扩散噪声以匹配图频率。这种各向异性扩散保留了重要的低频成分，维持了较高的信噪比。S-Diff进一步采用条件去噪网络编码用户交互，从噪声数据中恢复真实偏好。该方法在多个数据集上均取得优异表现。
原文摘要 · Abstract (English)
Recovering user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent distributions, they often fail to capture similar users' collective preferences effectively. Additionally, latent variables degrade into pure Gaussian noise during the forward process, lowering the signal-to-noise ratio, which in turn degrades performance. To address this, we propose S-Diff, inspired by graph-based collaborative filtering, better to utilize low-frequency components in the graph spectral domain. S-Diff maps user interaction vectors into the spectral domain and parameterizes diffusion noise to align with graph frequency. This anisotropic diffusion retains significant low-frequency components, preserving a high signal-to-noise ratio. S-Diff further employs a conditional denoising network to encode user interactions, recovering true preferences from noisy data. This method achieves strong results across multiple datasets.
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