Graph Convolution Networks (GCNs) are widely considered state-of-the-art for
collaborative filtering. Although several GCN-based methods have been proposed
and achieved state-of-the-art performance in various tasks, they can be
computationally expensive and time-consuming to train if too many layers are
created. However, since the linear GCN model can be interpreted as a
differential equation, it is possible to transfer it to an ODE problem. This
inspired us to address the computational limitations of GCN-based models by
designing a simple and efficient NODE-based model that can skip some GCN layers
to reach the final state, thus avoiding the need to create many layers. In this
work, we propose a Graph Neural Ordinary Differential Equation-based method for
Collaborative Filtering (GODE-CF). This method estimates the final embedding by
utilizing the information captured by one or two GCN layers. To validate our
approach, we conducted experiments on multiple datasets. The results
demonstrate that our model outperforms competitive baselines, including
GCN-based models and other state-of-the-art CF methods. Notably, our proposed
GODE-CF model has several advantages over traditional GCN-based models. It is
simple, efficient, and has a fast training time, making it a practical choice
for real-world situations.

我们提出了一种基于图神经常微分方程的协同过滤方法（GODE-CF），该方法通过利用一个或两个图卷积网络层捕获的信息来估计最终嵌入，并证明了该模型在多个数据集上优于竞争基线和其他最新的协同过滤方法。值得注意的是，我们的 GODE-CF 模型相对于传统的图卷积网络模型具有简单、高效和快速训练时间等多个优点，使其成为实际场景中的理想选择。