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.

我们提出了一种频率感知的图信号处理方法（FaGSP），用于协同过滤，它通过设计级联滤波模块和并行滤波模块，结合线性模型，更准确地建模用户偏好，并在预测准确性和训练效率方面展示了优越性。