论文标题
推荐系统中的图形神经网络：调查
Graph Neural Networks in Recommender Systems: A Survey
论文作者
论文摘要
随着在线信息的爆炸性增长，推荐系统起着减轻此类信息过载的关键作用。由于推荐系统的重要应用值，因此在该领域始终有新兴的作品。在推荐系统中，主要的挑战是从其交互和附带信息（如果有）中学习有效的用户/项目表示。最近，图形神经网络（GNN）技术已在推荐系统中广泛使用，因为推荐系统中的大多数信息本质上都具有图形结构，并且GNN在图形表示学习中具有优越性。本文旨在对基于GNN的推荐系统进行最新研究工作进行全面综述。具体而言，我们根据所使用的信息和建议任务的类型提供基于GNN的建议模型的分类法。此外，我们系统地分析了将GNN应用于不同类型的数据的挑战，并讨论了该领域中现有的工作方式如何应对这些挑战。此外，我们陈述了与该领域发展有关的新观点。我们在https://github.com/wusw14/gnn-in-rs中收集了代表性论文及其开源实现。
With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.
