图信号如何影响推荐：揭开低频和高频图信号的谜团
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
AI总结：
本文揭示了低频和高频图信号在推荐中的等效作用，提出频率信号标定器和空间翻转方法以提升推荐效果。
摘要：
谱图神经网络（GNNs）在建模图信号方面非常有效，其在推荐中的成功通常归因于低通滤波。然而，最近的研究强调了高频信号的重要性。低频和高频图信号在推荐中的作用仍然不明确。本文旨在通过研究图信号对推荐性能的影响来填补这一空白。我们理论证明了低频和高频图信号在推荐任务中的效果是等同的，因为两者都通过平滑用户-物品对之间的相似性来发挥作用。为了利用这一见解，我们提出了一个频率信号标定器，这是一个即插即用的模块，它调整图信号滤波函数以微调用户-物品对之间的平滑度，使其与任何GNN模型兼容。此外，我们识别并证明了基于图嵌入的方法无法完全捕捉图信号的特征。为了解决这一限制，引入了一种空间翻转方法以恢复图嵌入的表达能力。值得注意的是，我们证明了单独使用低频或高频图信号即可实现有效的推荐。在四个公开数据集上的广泛实验验证了我们所提出方法的有效性。代码可在https://github.com/mojosey/SimGCF上获得。
英文摘要：
Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent studies highlight the importance of high-frequency signals. The role of low-frequency and high-frequency graph signals in recommendation remains unclear. This paper aims to bridge this gap by investigating the influence of graph signals on recommendation performance. We theoretically prove that the effects of low-frequency and high-frequency graph signals are equivalent in recommendation tasks, as both contribute by smoothing the similarities between user-item pairs. To leverage this insight, we propose a frequency signal scaler, a plug-and-play module that adjusts the graph signal filter function to fine-tune the smoothness between user-item pairs, making it compatible with any GNN model. Additionally, we identify and prove that graph embedding-based methods cannot fully capture the characteristics of graph signals. To address this limitation, a space flip method is introduced to restore the expressive power of graph embeddings. Remarkably, we demonstrate that either low-frequency or high-frequency graph signals alone are sufficient for effective recommendations. Extensive experiments on four public datasets validate the effectiveness of our proposed methods. Code is avaliable at https://github.com/mojosey/SimGCF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
