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Special Issue: Data Management and Data Mining

• Special Section on Entity Resolution •

### User Account Linkage Across Multiple Platforms with Location Data

Wei Chen1, Member, CCF, ACM, Weiqing Wang2, Member, CCF, ACM, Hongzhi Yin3, Member, CCF, ACM Jun-Hua Fang1, Member, CCF, ACM, Lei Zhao1,*, Member, CCF, ACM

1. 1 Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou 215006, China;
2 Faculty of Information Technology, Monash University, Melbourne 3000, Australia;
3 School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia
• Received:2019-12-26 Revised:2020-06-08 Online:2020-07-20 Published:2020-07-20
• Contact: Lei Zhao E-mail:zhaol@suda.edu.cn
• About author:Wei Chen is currently a lecturer in the School of Computer Science and Technology, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2018. His research interests include heterogeneous information network analysis, crossplatform linkage and recommendation, spatio-temporal database, and knowledge graph embedding and refinement.
• Supported by:
This work was supported by Australian Research Council under Grant No. DP190101985, the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant Nos. 19KJA610002 and 19KJB520050, and the National Natural Science Foundation of China under Grant No. 61902270.

Linking user accounts belonging to the same user across different platforms with location data has received significant attention, due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage (e.g., cross-platform user profiling and recommendation). Different from most existing studies which only focus on user account linkage across two platforms, we propose a novel model ULMP (i.e., user account linkage across multiple platforms), with the goal of effectively and efficiently linking user accounts across multiple platforms with location data. Despite of the practical significance brought by successful user linkage across multiple platforms, this task is very challenging compared with the ones across two platforms. The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms. To tackle the problem, a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures. Then, in the pruned space, a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts. The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.

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