  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.
