Explainability of recommender systems has become essential to ensure users' trust and satisfaction. Various types of explainable recommender systems have been proposed including explainable graph-based recommender systems. This review paper discusses state-of-the-art approaches of these systems and categorizes them based on three aspects: learning methods, explaining methods, and explanation types. It also explores the commonly used datasets, explainability evaluation methods, and future directions of this research area. Compared with the existing review papers, this paper focuses on explainability based on graphs and covers the topics required for developing novel explainable graph-based recommender systems.

翻译：推荐系统的可解释性对于确保用户信任与满意度至关重要。目前已提出多种可解释推荐系统，包括基于图结构的可解释推荐系统。本综述论文系统论述了该领域的前沿方法，并从学习方法、解释方法与解释类型三个维度对其进行分类。同时探讨了常用数据集、可解释性评估方法以及该研究领域的未来发展方向。与现有综述相比，本文聚焦于基于图结构的可解释性研究，涵盖了开发新型可解释图基推荐系统所需的核心议题。