Explainable recommendation based on knowledge graph and multi-objective optimization

doi:10.1007/s40747-021-00315-y

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Explainable recommendation based on knowledge graph and multi-objective optimization
	Xie, Lijie 1; Hu, Zhaoming 1; Cai, Xingjuan 1; Zhang, Wensheng2 ; Chen, Jinjun 3
发表期刊	COMPLEX & INTELLIGENT SYSTEMS
ISSN	2199-4536
	2021-03-06
页码	12
通讯作者	Cai, Xingjuan(xingjuancai@163.com)
摘要	Recommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability.
关键词	Recommendation system Knowledge graph Multi-objective optimization Explainability
DOI	10.1007/s40747-021-00315-y
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Australian Research Council (ARC)[DP190101893] ; Australian Research Council (ARC)[DP170100136] ; Australian Research Council (ARC)[LP180100758]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D program of Shanxi Province (International Cooperation) ; Australian Research Council (ARC)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000625746400001
出版者	SPRINGER HEIDELBERG
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44165
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Cai, Xingjuan
作者单位	1.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China 3.Swinburne Univ Technol, Melbourne, Vic, Australia
推荐引用方式 GB/T 7714	Xie, Lijie,Hu, Zhaoming,Cai, Xingjuan,et al. Explainable recommendation based on knowledge graph and multi-objective optimization[J]. COMPLEX & INTELLIGENT SYSTEMS,2021:12.
APA	Xie, Lijie,Hu, Zhaoming,Cai, Xingjuan,Zhang, Wensheng,&Chen, Jinjun.(2021).Explainable recommendation based on knowledge graph and multi-objective optimization.COMPLEX & INTELLIGENT SYSTEMS,12.
MLA	Xie, Lijie,et al."Explainable recommendation based on knowledge graph and multi-objective optimization".COMPLEX & INTELLIGENT SYSTEMS (2021):12.