Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item characteristics and failed to utilize user and item high-order neighborhood information to model user preference, thus leading to sub-optimal performance. To address the above issues, 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 that work in a successive manner, to capture both unique and common user/item characteristics to more accurately model user preference. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood, to fully utilize high-order neighborhood information of users/items for more accurate user preference modeling. Finally, we combine these two modules via a linear model to further improve recommendation accuracy. Extensive experiments on six public datasets demonstrate the superiority of our method from the perspectives of prediction accuracy and training efficiency compared with state-of-the-art GCN-based recommendation methods and GSP-based recommendation methods.

翻译：基于图信号处理（GSP）的推荐算法因其高效性近期受到广泛关注。然而，这类方法未能考虑反映用户/物品独特特征的不同交互的重要性，也未能利用用户和物品的高阶邻域信息来建模用户偏好，从而导致性能次优。为解决上述问题，我们提出一种面向协同过滤的频率感知图信号处理方法（FaGSP）。首先，我们设计了一个级联滤波器模块，该模块由依次工作的理想高通滤波器和理想低通滤波器组成，用于同时捕捉用户/物品的独有特征和共性特征，从而更精确地建模用户偏好。其次，我们设计了一个并行滤波器模块，该模块包含两个能够轻松捕捉邻域层级结构的低通滤波器，以充分利用用户/物品的高阶邻域信息，实现更准确的用户偏好建模。最后，我们通过线性模型将这两个模块结合，进一步提升推荐准确性。在六个公开数据集上的大量实验表明，与当前最先进的基于GCN的推荐方法和基于GSP的推荐方法相比，本方法在预测准确性和训练效率方面均具有优越性。