通过用户交互顺序增强物品关系，提升推荐模型精度。

Graph-based collaborative filtering methods act as low-pass filters in the spectral domain and discard the intermediate-frequency components where community-level user preferences reside. Existing GSP-based methods address the loss through sophisticated filter designs, yet derive item representations from the user-item interaction matrix alone. The interaction matrix captures which items each user interacted with, but not which items appear close together in users' interaction sequences. We propose GSPRec, a graph spectral collaborative filtering framework that produces richer item spectral representations by incorporating item-item proximity derived from user interaction ordering before spectral filtering. GSPRec derives item-item edges from user interaction ordering and strengthens the edges through multi-hop diffusion with exponential decay. The unified graph topology incorporates the diffused edges alongside user-item interactions. The resulting Laplacian exposes intermediate-frequency structure that a Gaussian bandpass filter selectively amplifies. A low-pass filter retains broad popularity trends. Experiments on four real-world datasets show that GSPRec outperforms all graph CF baselines, with average improvements of 5.12% in NDCG@10. Ablation studies establish that graph construction and filter design are coupled. GSPRec without the bandpass filter falls below every GSP baseline, whereas GSPRec without item-item proximity still surpasses baselines.