Modeling Multi-factor Sequential User Behavior Data over Social Networks

doi:10.1049/cje.2016.03.025

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Modeling Multi-factor Sequential User Behavior Data over Social Networks
	Wang Peng 1,2,3; Zhang Peng 2; Zhou Chuang 2; Guo Li 2; Fang Binxing 2; Yang Tao 4
刊名	CHINESE JOURNAL OF ELECTRONICS
	2016-03-01
卷号	25 期号:2 页码:364-371
关键词	Malicious user detection User behavior Social networks Bayesian model Social influence
ISSN号	1022-4653
DOI	10.1049/cje.2016.03.025
英文摘要	Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors: user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesian model HES to model dynamic user behavior data. We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.
资助项目	NSFC[61370025] ; NSFC[61502479] ; 863 projects[2011AA01A103] ; National Basic Program of China (973 project)[2013CB329605] ; Strategic Leading Science and Technology Projects of Chinese Academy of Sciences[XDA06030200]
WOS研究方向	Engineering
语种	英语
出版者	TECHNOLOGY EXCHANGE LIMITED HONG KONG
WOS记录号	WOS:000372046100025
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/8651]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang Peng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, Beijing 100089, Peoples R China 3.Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China 4.China Informat Technol Secur Evaluat Ctr, Beijing 100085, Peoples R China
推荐引用方式 GB/T 7714	Wang Peng,Zhang Peng,Zhou Chuang,et al. Modeling Multi-factor Sequential User Behavior Data over Social Networks[J]. CHINESE JOURNAL OF ELECTRONICS,2016,25(2):364-371.
APA	Wang Peng,Zhang Peng,Zhou Chuang,Guo Li,Fang Binxing,&Yang Tao.(2016).Modeling Multi-factor Sequential User Behavior Data over Social Networks.CHINESE JOURNAL OF ELECTRONICS,25(2),364-371.
MLA	Wang Peng,et al."Modeling Multi-factor Sequential User Behavior Data over Social Networks".CHINESE JOURNAL OF ELECTRONICS 25.2(2016):364-371.