论文标题
在图表上推荐：从数据角度进行全面评论
Recommending on graphs: a comprehensive review from a data perspective
论文作者
论文摘要
基于图的学习方法的最新进展证明了它们在建模用户对推荐系统（RSS）的特征（RSS）方面的有效性。 RS中的大多数数据都可以组织成图表，其中各种对象（例如用户，项目和属性）明确或隐式连接并通过各种关系相互影响。这样的基于图形的组织为利用图形学习的潜在属性（例如随机步行和网络嵌入）技术带来了好处，以丰富用户和项目节点的表示，这是成功建议的重要因素。在本文中，我们提供了基于图学习的推荐系统（GLRS）的全面调查。具体而言，我们从数据驱动的角度开始，系统地对GLRS中的各种图进行分类并分析其特征。然后，我们讨论了最新的框架，重点是图形学习模块，以及它们如何应对实用的建议挑战，例如可扩展性，公平性，多样性，解释性等。最后，我们在这个快速增长的地区共享一些潜在的研究方向。
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.
