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.

翻译：在信息爆炸的时代，推荐系统是为用户提供个性化推荐的重要工具。推荐系统的核心是基于历史用户-物品交互数据预测用户未来的行为。由于能有效捕捉用户-物品交互数据中的高阶连接性，近年来利用图神经网络提升推荐系统预测性能的研究日益兴起。然而，经典矩阵分解和深度神经网络方法凭借其可扩展性优势，仍在实际大规模推荐系统中扮演重要角色。尽管存在图神经网络加速方案，但基于图神经网络的推荐系统能否达到与经典矩阵分解和深度神经网络方法同等的可扩展性仍是一个开放性问题。本文提出线性时间图神经网络，旨在扩展基于图神经网络的推荐系统，使其在保持图神经网络强大表达能力以实现卓越预测精度的同时，达到与经典矩阵分解方法相当的可扩展性。通过大量实验和消融研究验证了所提算法的有效性与可扩展性。基于PyTorch的实现已开放获取。