A hybrid recommendation system with many-objective evolutionary algorithm

doi:10.1016/j.eswa.2020.113648

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

	A hybrid recommendation system with many-objective evolutionary algorithm
	Cai, Xingjuan 1; Hu, Zhaoming 1; Zhao, Peng 1; Zhang, WenSheng2 ; Chen, Jinjun 3
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
	2020-11-30
卷号	159 页码:10
通讯作者	Cai, Xingjuan(xingjuancai@163.com)
摘要	Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation. This model enhances the robustness of recommendations by mixing three different basic recommendation technologies. Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively. Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse. (C) 2020 Elsevier Ltd. All rights reserved.
关键词	Recommendation systems Many-objective optimization Hybrid recommender algorithm Collaborative filtering
DOI	10.1016/j.eswa.2020.113648
关键词[WOS]	SWARM OPTIMIZATION ALGORITHM ; BAT ALGORITHM
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61663028] ; Natural Science Foundation of Shanxi Province[201801D121127] ; Key R&D program of Shanxi Province (High Technology)[201903D121119]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Key R&D program of Shanxi Province (High Technology)
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000583204100034
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：84[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/173211/41804
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, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China 3.Univ Technol Sydney, Sydney, NSW, Australia
推荐引用方式 GB/T 7714	Cai, Xingjuan,Hu, Zhaoming,Zhao, Peng,et al. A hybrid recommendation system with many-objective evolutionary algorithm[J]. EXPERT SYSTEMS WITH APPLICATIONS,2020,159:10.
APA	Cai, Xingjuan,Hu, Zhaoming,Zhao, Peng,Zhang, WenSheng,&Chen, Jinjun.(2020).A hybrid recommendation system with many-objective evolutionary algorithm.EXPERT SYSTEMS WITH APPLICATIONS,159,10.
MLA	Cai, Xingjuan,et al."A hybrid recommendation system with many-objective evolutionary algorithm".EXPERT SYSTEMS WITH APPLICATIONS 159(2020):10.