Abstract: Recommendation systems can obtain user preferences in massive data information to better achieve personalized recommendations, improve user physical examination and solve information overload on the Internet. However, it still suffers from cold start and data sparsity problems. A knowledge graph, as a structured knowledge base with a large number of entities and rich semantic relationships, can not only improve the accuracy of recommendation systems, but also provide the interpretability for recommendation items, thus enhancing users?? trust in recommendation systems, and providing new methods and ideas to solve a series of key problems in recommendation systems. This paper firstly studies and analyzes knowledge graph recommendation systems, classifies them into multi-domain knowledge graph recommendation systems and domain-specific knowledge graph recommendation systems based on the classification of application fields, and further classifies them according to the characteristics of these knowledge graph recommendation methods, and conducts quantitative and qualitative analyses for each type of methods. Secondly, this paper lists the datasets commonly used by knowledge graph recommendation systems in the application fields, and gives an overview of the size and characteristics of the datasets. Finally, this paper outlooks and summarizes the future research directions of knowledge graph recommendation systems.

Key words: knowledge graph, recommendation system, knowledge graph embedding, knowledge graph recommendation system dataset

摘要： 推荐系统可以在海量的数据信息中获取用户偏好，从而更好地实现个性化推荐，提高用户体检，以及解决互联网中的信息过载问题，但推荐系统仍然存在冷启动和数据稀疏问题。知识图谱作为一种拥有大量实体和丰富语义关系的结构化知识库，不但能够提高推荐系统的准确性，还能够为推荐项目提供可解释性，从而增强用户对推荐系统的信任度，为解决推荐系统中存在的一系列关键问题提供了新方法、新思路。首先针对知识图谱推荐系统进行研究与分析，以应用领域为分类依据将知识图谱推荐系统分为多领域知识图谱推荐系统和特定领域知识图谱推荐系统，同时根据这些知识图谱推荐方法的特点进一步分类，对每类方法进行定量分析和定性分析；之后列举出知识图谱推荐系统在应用领域中常用的数据集，对数据集的规模和特点进行概述；最后对知识图谱推荐系统未来的研究方向进行展望和总结。

关键词: 知识图谱, 推荐系统, 知识图谱嵌入, 知识图谱推荐系统数据集