IM2Vec: Representation learning-based preference maximization in geo-social networks

doi:10.1016/j.ins.2022.04.062

	IM2Vec: Representation learning-based preference maximization in geo-social networks
	Jin, Ziwei 1,2; Shang, Jiaxing 1,2; Ni, Wancheng3,4 ; Zhao, Liang 5; Liu, Dajiang 1,2; Qiang, Baohua 6; Xie, Wu 6; Min, Geyong 7
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
	2022-08-01
卷号	604 页码:170-196
通讯作者	Shang, Jiaxing(shangjx@cqu.edu.cn) ; Ni, Wancheng(wancheng.ni@ia.ac.cn)
摘要	Recent advancements in mobile technology have facilitated location-based social networks. The location-based influence maximization problem, which aims to find top influential seed users for promoting a target location to attract the most individuals, has drawn increasing attention. However, the existing studies largely neglect the importance of user preference, which considerably hinders their practicability. In addition, time efficiency is a critical issue for handling large-scale datasets. To address the above problems, we propose a new framework named IM2Vec, which incorporates representation learning into location-based influence maximization problem. Specifically, we first propose a representation learning model, All2Vec, to capture user preferences for the target location from check-in records, which takes both user preference and geographical location influence into consideration. Then, based on the learned user preferences, we extend the reverse influence sampling (RIS) model and propose a highly efficient preference maximization algorithm, which ensures a (1 - 1/e - epsilon)-approximate solution with a substantially lower sample size. The experimental results of the two tasks (future visitor prediction and influence maximization) on two real geo-social networks show that the All2Vec model achieves considerably higher accuracy in future visitor prediction, and IM2Vec exhibits a higher influence spread and a lower running time than the state-of-the-art baselines. (C) 2022 Elsevier Inc. All rights reserved.
关键词	Influence maximization Representation learning Location-based social networks Diffusion model Reverse influence sampling
DOI	10.1016/j.ins.2022.04.062
关键词[WOS]	EFFICIENT ; LOCATION ; SEEDS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61966008] ; National Natural Science Foundation of China[U2033213] ; Guangxi Key Laboratory of Trusted Software[kx201702] ; Science Foundation of Liaoning Province[2020-MS-237] ; Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring Research Fund (Fujian Normal University)[202001]
项目资助者	National Natural Science Foundation of China ; Guangxi Key Laboratory of Trusted Software ; Science Foundation of Liaoning Province ; Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring Research Fund (Fujian Normal University)
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000803791400010
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：6[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/173211/49498
专题	复杂系统认知与决策实验室_智能系统与工程
通讯作者	Shang, Jiaxing; Ni, Wancheng
作者单位	1.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China 2.Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Shenyang Aerosp Univ, Sch Comp Sci, Shenyang 110136, Peoples R China 6.Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China 7.Univ Exeter, Sch Comp Sci, Exeter EH10 9FH, England
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jin, Ziwei,Shang, Jiaxing,Ni, Wancheng,et al. IM2Vec: Representation learning-based preference maximization in geo-social networks[J]. INFORMATION SCIENCES,2022,604:170-196.
APA	Jin, Ziwei.,Shang, Jiaxing.,Ni, Wancheng.,Zhao, Liang.,Liu, Dajiang.,...&Min, Geyong.(2022).IM2Vec: Representation learning-based preference maximization in geo-social networks.INFORMATION SCIENCES,604,170-196.
MLA	Jin, Ziwei,et al."IM2Vec: Representation learning-based preference maximization in geo-social networks".INFORMATION SCIENCES 604(2022):170-196.