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

文献类型：期刊论文

作者Jin, Ziwei6,7; Shang, Jiaxing6,7; Ni, Wancheng4,5; Zhao, Liang3; Liu, Dajiang6,7; Qiang, Baohua2; Xie, Wu2; Min, Geyong1
刊名INFORMATION SCIENCES
出版日期2022-08-01
卷号604页码:170-196
关键词Influence maximization Representation learning Location-based social networks Diffusion model Reverse influence sampling
ISSN号0020-0255
DOI10.1016/j.ins.2022.04.062
通讯作者Shang, Jiaxing(shangjx@cqu.edu.cn) ; Ni, Wancheng(wancheng.ni@ia.ac.cn)
英文摘要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.
WOS关键词EFFICIENT ; LOCATION ; SEEDS
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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000803791400010
出版者ELSEVIER SCIENCE INC
资助机构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)
源URL[http://ir.ia.ac.cn/handle/173211/49497]  
专题智能系统与工程
通讯作者Shang, Jiaxing; Ni, Wancheng
作者单位1.Univ Exeter, Sch Comp Sci, Exeter EH10 9FH, England
2.Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China
3.Shenyang Aerosp Univ, Sch Comp Sci, Shenyang 110136, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
6.Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China
7.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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