Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling

doi:10.1109/TKDE.2016.2637898

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling
	Liu, Siyuan 1; Wang, Shuhui 2
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2017-04-01
卷号	29 期号:4 页码:898-911
关键词	Community detection trajectory multiple information sources semantic information
ISSN号	1041-4347
DOI	10.1109/TKDE.2016.2637898
英文摘要	In this paper, we detect communities from trajectories. Existing algorithms for trajectory clustering usually rely on simplex representation and a single proximity-related metric. Unfortunately, additional information markers (e.g., social interactions or semantics in the spatial layout) are ignored, leading to the inability to fully discover the communities in trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) are especially useful in capturing latent relationships among community members. To overcome this limitation, we propose TODMIS, a general framework for Trajectory-based cOmmunity Detection by diffusion modeling on Multiple Information Sources. TODMIS combines additional information with raw trajectory data and construct the diffusion process on multiple similarity metrics. It also learns the consistent graph Laplacians by constructing the multi-modal diffusion process and optimizing the heat kernel coupling on each pair of similarity matrices from multiple information sources. Then, dense sub-graph detection is used to discover the set of distinct communities (including community size) on the coupled multi-graph representation. At last, based on the community information, we propose a novel model for online recommendation. We evaluate TODMIS and our online recommendation methods using different real-life datasets. Experimental results demonstrate the effectiveness and efficiency of our methods.
资助项目	National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61673241] ; National Natural Science Foundation of China[61303160] ; Bureau of Frontier Sciences and Education (CAS)[QYZDJ-SSW-SYS013] ; Basic Research Program of Shenzhen[JCYJ20140610152828686]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000397581000014
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/7295]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Siyuan
作者单位	1.Penn State Univ, Smeal Coll Business, University Pk, PA 16802 USA 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Siyuan,Wang, Shuhui. Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(4):898-911.
APA	Liu, Siyuan,&Wang, Shuhui.(2017).Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(4),898-911.
MLA	Liu, Siyuan,et al."Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.4(2017):898-911.