User Identification Across Social Networks Based on User Trajectory

Hongchang CHEN; Qian XU; Ruiyang HUANG; Xiaotao CHENG; Zheng WU

doi:10.11999/JEIT180130

Volume 40 Issue 11

Oct. 2018

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Hongchang CHEN, Qian XU, Ruiyang HUANG, Xiaotao CHENG, Zheng WU. User Identification Across Social Networks Based on User Trajectory[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2758-2764. doi: 10.11999/JEIT180130

Citation:

Hongchang CHEN, Qian XU, Ruiyang HUANG, Xiaotao CHENG, Zheng WU. User Identification Across Social Networks Based on User Trajectory[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2758-2764. doi: 10.11999/JEIT180130

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User Identification Across Social Networks Based on User Trajectory

doi: 10.11999/JEIT180130

National Digital Switching Engineering & Technological Research Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61521003)

Received Date: 2018-01-30
Rev Recd Date: 2018-06-11

Available Online: 2018-06-30

Publish Date: 2018-11-01

Abstract

Abstract

The performance of trajectory based user identification is poor since the existing methods ignore the order feature of location sequence. To solve this problem, a Cross Domain Trajectory matching algorithm based on Paragraph2vec (CDTraj2vec) is proposed. Firstly, the user trajectory is transformed to the grid representation which is easy to handle. The PV-DM model in the Paragraph2vec algorithm is utilized for extracting order feature of location sequence in trajectory. Then the original user trajectories are divided by a certain time size and distance scale to construct a training sample suitable for training PV-DM model. The PV-DM model is trained by different types of training samples, and the vector representation of the user trajectories is obtained. Finally, the matching of the trajectory is determined by the user trajectory vector. Experimental results on BrightKite shows that the F-measure is improved by 2%～4% compared with the existing frequency based and distance based algorithm. The proposed algorithm can effectively extract the order feature of location sequence, and realize the trajectory based user identification across social networks.
- Social networks,
- User identification,
- Trajectory similarity,
- Paragraph2vec

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References(15)

References

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