GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation

• URL: http://arxiv.org/abs/2505.11552v1
• Date: Thu, 15 May 2025 15:49:56 GMT
• Title: GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation
• Authors: Ahmad Bin Rabiah, Julian McAuley,
• Abstract summary: We introduce GSPRec, a graph spectral model that integrates temporal transitions through sequentially-informed graph construction.<n> GSPRec encodes item transitions via multi-hop diffusion to enable the use of symmetric Laplacians for spectral processing.<n>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.
• Score: 18.379840329713403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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