Review of Recommendation Systems Based on Knowledge Graph

被引：0
|
作者：
Dongliang Z. [1 ]
Yi W. [1 ]
Zichen W. [1 ]
机构：
[1] Chengdu Library and Information Center, Chinese Academy of Sciences
[2] Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences
来源：
Data Analysis and Knowledge Discovery | 2021年 / 5卷 / 12期
关键词：
Data Mining; Knowledge Graph; Recommendation System;
D O I：
10.11925/infotech.2096-3467.2021.0516
中图分类号：
学科分类号：
摘要：
[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. © 2021, Chinese Academy of Sciences. All rights reserved.
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页码：1 / 13
页数：12
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