Graph Neural Controlled Differential Equations For Collaborative Filtering

TL;DR

This paper introduces Graph Neural Controlled Differential Equations for collaborative filtering, enhancing recommendation performance by incorporating continuous weight control in neural ODE frameworks.

Contribution

It proposes a novel neural ODE-based method with continuous weight control, improving adaptability and recommendation accuracy over existing fixed-weight models.

Findings

Outperforms GCN-based models in recommendation tasks

Surpasses existing Graph ODE-based methods in accuracy

Demonstrates effectiveness across multiple datasets

Abstract

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.