Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.

翻译：近年来，基于图的学习方法在推荐系统中对用户偏好和物品特征的建模有效性已得到充分验证。推荐系统中的大部分数据可被组织为图结构，其中各类对象（如用户、物品和属性）通过显式或隐式关联相互影响。这种基于图的数据组织方式有助于利用图学习技术（如随机游走和网络嵌入）挖掘潜在特性，从而丰富用户节点与物品节点的表征——这是实现成功推荐的关键要素。本文对基于图学习的推荐系统进行了全面综述。具体而言，我们采用数据驱动的视角，系统分类了图推荐系统中各类图结构并分析其特征；随后聚焦图学习模块，讨论当前前沿框架如何应对可扩展性、公平性、多样性、可解释性等实际推荐挑战；最后，我们展望了这一快速发展领域中的若干潜在研究方向。