Improving social and behavior recommendations via network embedding

doi:10.1016/j.ins.2019.12.038

	Improving social and behavior recommendations via network embedding
	Zhao, Weizhong 1,6,7; Ma, Huifang 2; Li, Zhixin 3; Ao, Xiang 4,8; Li, Ning 5
	2020-04-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	516 页码:125-141
摘要	With the rapid development of information technology, information is generated at an unprecedented rate. Users are in great need of recommender systems to provide the potential friends or interested items for them. Social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in real-world applications. Although researchers have proposed various models for each task, a unified model to address both tasks elegantly and effectively is still in demand. In this paper, we propose a model called SBRNE which integrates social and behavior recommendations into a unified framework through modeling social and behavior information simultaneously. Specifically, SBRNE models social and behavior information simultaneously via employing users' latent interests as a bridge, and derives improved performance on both social and behavior recommendation tasks. In addition, by introducing an efficient network embedding procedure, users' latent representations are advanced, and effectiveness and efficiency of recommendation tasks are improved accordingly. Results on both real-world and synthetic datasets demonstrate that: 1). SBRNE outperforms selected baselines on social and behavior recommendation tasks; 2). SBRNE performs stable on recommendation tasks for cold-start users; 3). The network embedding procedure can improve the effectiveness of SBRNE; 4). The hyper-parameter learning procedure can improve both the effectiveness and efficiency of SBRNE. (C) 2019 Elsevier Inc. All rights reserved.
关键词	Social recommendation Behavior recommendation Network embedding Probabilistic matrix factorization
DOI	10.1016/j.ins.2019.12.038
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61976204] ; National Natural Science Foundation of China[61966004] ; National Natural Science Foundation of China[61932008] ; National Natural Science Foundation of China[61802404] ; National Natural Science Foundation of China[61762078] ; National Natural Science Foundation of China[61663004] ; National Natural Science Foundation of China[61532008] ; Wuhan Science and Technology Program[2019010701011392] ; Fundamental Research Funds for the Central Universities[CCNU19TD004] ; Guangxi Key Laboratory of Trusted Software[kx201905]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000515432200008
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14628
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Weizhong
作者单位	1.Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China 2.Northeast Normal Univ, Coll Comp Sci & Engn, Lanzhou, Peoples R China 3.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin, Peoples R China 4.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 6.Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Peoples R China 7.Guilin Univ Elect Technol, Guangvi Key Lab Trusted Software, Guilin, Peoples R China 8.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Weizhong,Ma, Huifang,Li, Zhixin,et al. Improving social and behavior recommendations via network embedding[J]. INFORMATION SCIENCES,2020,516:125-141.
APA	Zhao, Weizhong,Ma, Huifang,Li, Zhixin,Ao, Xiang,&Li, Ning.(2020).Improving social and behavior recommendations via network embedding.INFORMATION SCIENCES,516,125-141.
MLA	Zhao, Weizhong,et al."Improving social and behavior recommendations via network embedding".INFORMATION SCIENCES 516(2020):125-141.