Linear-Time Graph Neural Networks for Scalable Recommendations
This work addresses the scalability problem for large-scale recommender systems, enabling GNN-based approaches to be more practical in real-world applications, though it is incremental in improving efficiency.
The paper tackles the scalability challenge of Graph Neural Networks (GNNs) in recommender systems by proposing a Linear-Time Graph Neural Network (LTGNN) that achieves comparable scalability to classic Matrix Factorization methods while maintaining superior prediction accuracy, as validated through extensive experiments.
In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. Nonetheless, classic Matrix Factorization (MF) and Deep Neural Network (DNN) approaches still play an important role in real-world large-scale recommender systems due to their scalability advantages. Despite the existence of GNN-acceleration solutions, it remains an open question whether GNN-based recommender systems can scale as efficiently as classic MF and DNN methods. In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy. Extensive experiments and ablation studies are presented to validate the effectiveness and scalability of the proposed algorithm. Our implementation based on PyTorch is available.