让推荐系统的图神经网络动态调整权重，提升预测精度
Graph Neural Controlled Differential Equations For Collaborative Filtering
- 用连续控制机制动态调节图卷积权重，避免固定权重限制
- 在多个数据集上优于传统GCN和现有图微分方程模型
- 适合需要高精度推荐的工业场景或研究者优化图学习框架
图卷积网络（GCNs）被广泛认为是推荐系统中的前沿方法。尽管已有研究尝试将协同过滤（CF）引入神经微分方程（Neural ODE）框架，但这些方法沿用LightGCN思想，采用无权或离散权重矩阵。我们指出，权重控制对基于神经微分方程的方法至关重要：为每个节点定制图卷积需持续调整权重，而固定或离散权重无法随时间动态变化，导致适应性下降，影响推荐效果。为此，本文提出一种新方法——用于协同过滤的图神经控制微分方程（CDE-CF），通过在神经微分方程中引入连续权重控制，实现更优的图卷积。我们在多个数据集上进行实验，结果表明该方法显著优于基线模型，包括基于GCN的模型及当前最先进的图微分方程方法。
原文摘要 · Abstract (English)
Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply collaborative filtering (CF) into the Neural ODE framework. These studies follow the same idea as LightGCN, which removes the weight matrix or with a discrete weight matrix. However, we argue that weight control is critical for neural ODE-based methods. The importance of weight in creating tailored graph convolution for each node is crucial, and employing a fixed/discrete weight means it cannot adjust over time within the ODE function. This rigidity in the graph convolution reduces its adaptability, consequently hindering the performance of recommendations. In this study, to create an optimal control for Neural ODE-based recommendation, we introduce a new method called Graph Neural Controlled Differential Equations for Collaborative Filtering (CDE-CF). Our method improves the performance of the Graph ODE-based method by incorporating weight control in a continuous manner. To evaluate our approach, we conducted experiments on various datasets. The results show that our method surpasses competing baselines, including GCNs-based models and state-of-the-art Graph ODE-based methods.
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