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.

翻译：图卷积网络（GCNs）被广泛认为是协同过滤的最先进方法。尽管已有多种基于GCN的方法被提出并在各项任务中取得领先性能，但若构建过多层数，其训练过程可能计算成本高昂且耗时。然而，由于线性GCN模型可被解释为微分方程，因此可将其转化为常微分方程（ODE）问题。这启发我们通过设计一种简单高效的基于神经ODE（NODE）的模型来克服GCN类模型的计算局限，该模型可跳过部分GCN层直接达到最终状态，从而避免构建过多层数。本文提出一种基于图神经普通微分方程的协同过滤方法（GODE-CF）。该方法通过利用一至两层GCN层捕获的信息来估计最终嵌入。为验证方法的有效性，我们在多个数据集上进行了实验。结果表明，我们的模型优于包括基于GCN的模型及其他最先进协同过滤方法在内的竞争基线。值得注意的是，我们提出的GODE-CF模型相较于传统GCN类模型具有多项优势：其简单、高效、训练速度快，使其成为实际应用场景中的实用选择。