Review of Recommendation Systems Based on Knowledge Graph
Zhu Dongliang,Wen Yi(),Wan Zichen
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041,China Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190,China
[Objective] This paper reviewed the latest achievements of recommendation systems based on the knowledge graph. [Coverage] We used “knowledge graph”, “KG”, “recommendation system”, “RS”, and “recommended system” as key words to search the Web of Science, CNKI, Wanfang and other scholarly databases. A total of 70 documents were reviewed. [Methods] First, we summarized the classification of recommendation algorithms based on knowledge graphs. Then, we sorted the development history of recommendation systems using different types of algorithms. Finally, we discussed the typical algorithms and their future development trends. [Results] The reviewed recommendation systems were based on connection, embedding and hybrid methods. The three types of algorithms have advantages and disadvantages in different scenarios. Maximizing the utilization of graph information and reducing the computing power consumption is the future direction. [Limitations] We did not include the commercial examples of the recommendation systems. [Conclusions] The knowledge graph and machine learning could effectively improve the traditional recommendation algorithms.
朱冬亮, 文奕, 万子琛. 基于知识图谱的推荐系统研究综述*[J]. 数据分析与知识发现, 2021, 5(12): 1-13.
Zhu Dongliang, Wen Yi, Wan Zichen. Review of Recommendation Systems Based on Knowledge Graph. Data Analysis and Knowledge Discovery, 2021, 5(12): 1-13.
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