Collaborative filtering recommendation method based on graph convolutional neural networks

Abstract

In the rapidly advancing information technology era, information overload poses a significant challenge. Recommender systems offer a partial solution, yet traditional methods grapple with issues like sparse data and accuracy. For this reason, this paper introduces a novel approach—a high-order graph convolutional collaborative filtering model. This model employs a subgraph generation module to enhance the importance of neighbor nodes during high-order graph convolutions. Our approach yields enhanced embeddings by embedding user-item interaction information using graph techniques, stacking multi-layer graph convolutional networks to capture complex interactions, and leveraging both initial and convoluted embeddings. This paper introduces a constraint loss function to address over-smoothing in graph-based recommendations. Our method's effectiveness is confirmed through extensive experiments on three real-world datasets