摘要
【目的】对基于知识图谱的推荐系统相关成果进行归纳梳理和展望总结。【文献范围】以"knowledge graph"、"KG"、"recommendation system"、"RS"、"recommended system"、"知识图谱"、"推荐系统"等关键词在Web of Science、中国知网、万方等文献数据库中进行检索,经过文献筛选,对其中的70篇文献进行研究总结。【方法】归纳总结基于知识图谱的推荐算法分类,对不同算法分类下的推荐系统发展历程进行梳理,介绍典型算法并对未来发展前景进行展望。【结果】基于知识图谱的推荐系统按照算法思想差异可以分为基于连接的推荐、基于嵌入的推荐和基于混合的推荐。三种算法思想在不同的使用场景各有优缺点,如何充分利用图谱信息的同时减少算力消耗,未来值得关注。【局限】由于渠道限制,未能得到基于知识图谱的推荐系统一定数量的商业落地实例进行剖析对比。【结论】基于知识图谱的推荐系统有效改善了传统推荐算法的效果,结合机器学习思想的推荐算法值得进一步探索,如何在有效范围内降低模型消耗也需要考虑。
[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.
作者
朱冬亮
文奕
万子琛
Zhu Dongliang;Wen Yi;Wan Ziehen(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)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第12期1-13,共13页
Data Analysis and Knowledge Discovery
基金
中国科学院文献情报能力建设专项(项目编号:Y9290002-3.5.3)的研究成果之一。
关键词
知识图谱
推荐系统
数据挖掘
Knowledge Graph
Recommendation System
Data Mining
作者简介
通讯作者:文奕(Wen Yi),ORCID:0000-0002-6520-2733,E-mail:weny@clas.ac.cn。
