Graph-based recommendation systems are effective at modeling collaborative patterns but often suffer from two limitations: overreliance on low-pass filtering, which suppresses user-specific signals, and omission of sequential dynamics in graph construction. We introduce GSPRec, a graph spectral model that integrates temporal transitions through sequentially-informed graph construction and applies frequency-aware filtering in the spectral domain. GSPRec encodes item transitions via multi-hop diffusion to enable the use of symmetric Laplacians for spectral processing. To capture user preferences, we design a dual-filtering mechanism: a Gaussian bandpass filter to extract mid-frequency, user-level patterns, and a low-pass filter to retain global trends. Extensive experiments on four public datasets show that GSPRec consistently outperforms baselines, with an average improvement of 6.77% in NDCG@10. Ablation studies show the complementary benefits of both sequential graph augmentation and bandpass filtering.

翻译：基于图的推荐系统能有效建模协同模式，但常受限于两个问题：过度依赖低通滤波（这会抑制用户特异性信号）以及图构建中忽略序列动态性。本文提出GSPRec，一种图谱模型，它通过序列感知的图构建整合时序转移关系，并在谱域应用频率感知滤波。GSPRec通过多跳扩散编码物品转移关系，从而能使用对称拉普拉斯矩阵进行谱处理。为捕捉用户偏好，我们设计了双滤波机制：高斯带通滤波器用于提取中频的用户层级模式，低通滤波器则用于保留全局趋势。在四个公开数据集上的大量实验表明，GSPRec持续优于基线模型，NDCG@10平均提升6.77%。消融实验验证了序列图增强与带通滤波的互补优势。