论文标题
推荐系统的图形学习方法：评论
Graph Learning Approaches to Recommender Systems: A Review
论文作者
论文摘要
近年来见证了基于图学习的推荐系统（GLR）的新兴主题的快速发展。 GLR主要采用先进的图形学习方法来建模用户的偏好和意图，以及项目的特征和推荐系统的受欢迎程度（RS）。与常规RS不同，包括基于内容的过滤和协作过滤的方式不同，GLR建立在简单或复杂的图表上，在这些图形上，各种对象（例如用户，项目和属性）明确或隐式连接。随着图形学习的快速发展，探索和利用图中的均质或异质关系是建立高级RS的有希望的方向。在本文中，我们对GLR的系统进行了系统的审查，如何从图表中获取知识，以提高建议的准确性，可靠性和解释性。首先，我们对GLR进行表征和形式化，然后总结并分类该新研究领域的主要挑战。然后，我们调查该地区最新和重要的发展。最后，我们在这个充满活力的地区共享一些新的研究方向。
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics and popularity for Recommender Systems (RS). Differently from conventional RS, including content based filtering and collaborative filtering, GLRS are built on simple or complex graphs where various objects, e.g., users, items, and attributes, are explicitly or implicitly connected. With the rapid development of graph learning, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building advanced RS. In this paper, we provide a systematic review of GLRS, on how they obtain the knowledge from graphs to improve the accuracy, reliability and explainability for recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges in this new research area. Then, we survey the most recent and important developments in the area. Finally, we share some new research directions in this vibrant area.
