Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification

doi:10.1016/j.neunet.2020.08.015

	Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification
	Xi, Dongbo 1,3; Zhuang, Fuzhen 1,2; Liu, Yanchi 5; Zhu, Hengshu 6; Zhao, Pengpeng 7; Tan, Chang 8; He, Qing 1,4
	2020-12-01
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	132 页码:75-83
摘要	Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past in-formation for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance. (c) 2020 Elsevier Ltd. All rights reserved.
关键词	Global transition patterns Personal preferences Missing POI category identification
DOI	10.1016/j.neunet.2020.08.015
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61773361] ; Project of Youth Innovation Promotion Association CAS, China[2017146]
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000590619800007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：7[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/2XEOYT63/16537
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Xiamen Data Intelligence Acad, ICT, Xiamen, Peoples R China 3.Meituan Dianping Grp, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Rutgers State Univ, Management Sci & Informat Syst, New Brunswick, NJ USA 6.Baidu Inc, Beijing, Peoples R China 7.Soochow Univ, Suzhou, Peoples R China 8.IFLYTEK, Hefei, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Xi, Dongbo,Zhuang, Fuzhen,Liu, Yanchi,et al. Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification[J]. NEURAL NETWORKS,2020,132:75-83.
APA	Xi, Dongbo.,Zhuang, Fuzhen.,Liu, Yanchi.,Zhu, Hengshu.,Zhao, Pengpeng.,...&He, Qing.(2020).Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification.NEURAL NETWORKS,132,75-83.
MLA	Xi, Dongbo,et al."Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification".NEURAL NETWORKS 132(2020):75-83.