ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning

• URL: http://arxiv.org/abs/2604.22549v1
• Date: Fri, 24 Apr 2026 13:39:51 GMT
• Title: ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
• Abstract summary: We show that graph filter learning is viable and generalizable, paving the way for more expressive graph neural networks in collaborative filtering.<n>Our findings demonstrate that graph filter learning is viable and generalizable, paving the way for more expressive graph neural networks in collaborative filtering.
• Score: 38.822669399680166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show that this challenge stems from a bias in traditional recommendation objectives, which induces a spectral phenomenon termed low-frequency explosion, thereby fundamentally hindering the effective learning of graph filters. To overcome this limitation, we propose a novel adaptive spectral graph collaborative filtering framework (ASPIRE) based on a bi-level optimization objective. Guided by our theoretical analysis, we disentangle the filter learning objective, which in turn leads to excellent recommendation performance, spectral adaptivity, and training stability in practice. Extensive experiments show our learned filters match the performance of carefully engineered task-specific designs. Furthermore, ASPIRE is equally effective in LLM-powered collaborative filtering. Our findings demonstrate that graph filter learning is viable and generalizable, paving the way for more expressive graph neural networks in collaborative filtering.

Related papers

• Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily [48.135717446964385]
  Spectral Graph Neural Networks (SGNNs) have achieved remarkable performance in tasks such as node classification.<n>We propose a novel framework that leverages Large Language Models (LLMs) to estimate the homophily level of a graph.<n>Our framework consistently improves performance over strong SGNN baselines.
  arXiv  Detail & Related papers  (2025-06-17T06:17:19Z)
• GSPRec: Temporal-Aware Graph Spectral Filtering for Recommendation [19.275813818441506]
  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.
  arXiv  Detail & Related papers  (2025-05-15T15:49:56Z)
• Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection [59.042018542376596]
  Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors.<n>Anomaly-Aware Pre-Training and Fine-Tuning (APF) is a framework to mitigate the challenges in GAD.<n> Comprehensive experiments on 10 benchmark datasets validate the superior performance of APF in comparison to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-19T09:57:35Z)
• Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach [58.8524608686851]
  Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for homophilic graphs and a high-pass filter for heterophilic graphs. We introduce a novel GNN framework Node-MoE that utilizes a mixture of experts to adaptively select the appropriate filters for different nodes.
  arXiv  Detail & Related papers  (2024-06-05T17:12:38Z)
• Frequency-aware Graph Signal Processing for Collaborative Filtering [26.317108637430664]
  We propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood.
  arXiv  Detail & Related papers  (2024-02-13T12:53:18Z)
• Shape-aware Graph Spectral Learning [36.63516222161871]
  Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs. Some works empirically show that the preferred graph frequency is related to the graph homophily level. This relationship between graph frequency and graphs with homophily/heterophily has not been systematically analyzed and considered in existing spectral GNNs. We propose shape-aware regularization on a Newton Interpolation-based spectral filter that can learn an arbitrary spectral filter and incorporate prior knowledge about the desired shape of the corresponding homophily level.
  arXiv  Detail & Related papers  (2023-10-16T04:57:30Z)
• Direct design of biquad filter cascades with deep learning by sampling random polynomials [5.1118282767275005]
  In this work, we learn a direct mapping from the target magnitude response to the filter coefficient space with a neural network trained on millions of random filters. We demonstrate our approach enables both fast and accurate estimation of filter coefficients given a desired response. We compare our method against existing methods including modified Yule-Walker and gradient descent and show IIRNet is, on average, both faster and more accurate.
  arXiv  Detail & Related papers  (2021-10-07T17:58:08Z)
• Learning Versatile Convolution Filters for Efficient Visual Recognition [125.34595948003745]
  This paper introduces versatile filters to construct efficient convolutional neural networks. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters.
  arXiv  Detail & Related papers  (2021-09-20T06:07:14Z)
• Unsharp Mask Guided Filtering [53.14430987860308]
  The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering. We propose a new and simplified formulation of the guided filter inspired by unsharp masking. Our formulation enjoys a filtering prior to a low-pass filter and enables explicit structure transfer by estimating a single coefficient.
  arXiv  Detail & Related papers  (2021-06-02T19:15:34Z)
• Graph Neural Networks with Adaptive Frequency Response Filter [55.626174910206046]
  We develop a graph neural network framework AdaGNN with a well-smooth adaptive frequency response filter. We empirically validate the effectiveness of the proposed framework on various benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-26T19:31:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.