# GSPRec: On Improving Item Representations in Graph Signal Processing for Collaborative Filtering

arXiv: https://arxiv.org/abs/2505.11552 · HTML: https://mlsanity.com/paper/2505.11552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 21 of 21)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Diffusion Improves Graph Learning](https://mlsanity.com/paper/1911.05485/) (2019)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://mlsanity.com/paper/2001.10167/) (2020)
- [Learning graphs from data: A signal representation perspective](https://mlsanity.com/paper/1806.00848/) (2018)
- [UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2110.15114/) (2021)
- [SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://mlsanity.com/paper/2109.12613/) (2021)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Interest-aware Message-Passing GCN for Recommendation](https://mlsanity.com/paper/2102.10044/) (2021)
- [Personalized Graph Signal Processing for Collaborative Filtering](https://mlsanity.com/paper/2302.02113/) (2023)
- [Design of graph filters and filterbanks](https://mlsanity.com/paper/1711.02046/) (2017)
- [On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://mlsanity.com/paper/2306.03624/) (2023)
- [Lightweight Embeddings for Graph Collaborative Filtering](https://mlsanity.com/paper/2403.18479/) (2024)
- [Bridging Conversational and Collaborative Signals for Conversational Recommendation](https://mlsanity.com/paper/2412.06949/) (2024)
- [Frequency-aware Graph Signal Processing for Collaborative Filtering](https://mlsanity.com/paper/2402.08426/) (2024)
- [Graph Signal Processing: Filter Design and Spectral Statistics](https://mlsanity.com/paper/1802.10145/) (2018)

## Abstract (arXiv)

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.