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Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding
Liu, Yang1,2; Ao, Xiang1,2; Dong, Linfeng1,2; Zhang, Chao3; Wang, Jin4; He, Qing1,2
2022
发表期刊IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN1041-4347
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
DOI10.1109/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
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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.
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