Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding

doi:10.1109/TKDE.2020.2983892

	Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding
	Liu, Yang 1,2; Ao, Xiang 1,2; Dong, Linfeng 1,2; Zhang, Chao 3; Wang, Jin 4; He, Qing 1,2
	2022
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	34 期号:1 页码:462-474
摘要	With the ever-increasing urbanization process, modeling people's spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct two graphs from raw data records to represent the user interaction graph layer and activity graph layer and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. The key notion behind our method is a novel hierarchical embedding framework with meta-graphs connecting different layers. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction, but also captures cross-modal proximity at a finer granularity.
关键词	Spatiotemporal activity mobile data cross-modal hierarchical embedding
DOI	10.1109/TKDE.2020.2983892
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China[61976204] ; National Natural Science Foundation of China[U1811461] ; Project of Youth Innovation Promotion Association CAS ; Natural Science Foundation of Chongqing[cstc2019jcyj-msxmX0149]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000728576400032
出版者	IEEE COMPUTER SOC
引用统计	被引频次：11[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/2XEOYT63/18343
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ao, Xiang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Georgia Tech, Coll Comp, Atlanta, GA 30332 USA 4.Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA
推荐引用方式 GB/T 7714	Liu, Yang,Ao, Xiang,Dong, Linfeng,et al. Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(1):462-474.
APA	Liu, Yang,Ao, Xiang,Dong, Linfeng,Zhang, Chao,Wang, Jin,&He, Qing.(2022).Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(1),462-474.
MLA	Liu, Yang,et al."Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.1(2022):462-474.