Abstract
Sources of complementary information are connected when we link the user accounts belonging to the same user across different domains or devices. The expanded information promotes the development of a wide range of applications, such as cross-domain prediction, cross-domain recommendation, and advertisement. Due to the great significance of user account linkage, there are increasing research works on this study. With the widespread popularization of GPS-enabled mobile devices, linking user accounts with location data has become an important and promising research topic. Being different from most existing studies in this domain that only focus on the effectiveness, we propose novel approaches to improve both effectiveness and efficiency of user account linkage. In this paper, a kernel density estimation (KDE) based method has been proposed to improve the accuracy by alleviating the data sparsity problem in measuring users' similarities. To improve the efficiency, we develop a grid-based structure to organize location data to prune the search space. The extensive experiments conducted on two real-world datasets demonstrate the superiority of the proposed approach in terms of both effectiveness and efficiency compared with the state-of-Art methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 |
| Subtitle of host publication | 16–19 April 2018 Paris, France |
| Editors | Panos K. Chrysanthis, Jens Dittrich, Beng Chin Ooi |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1085-1096 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781538655207 |
| ISBN (Print) | 9781538655214 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | IEEE International Conference on Data Engineering 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 Conference number: 34th https://icde2018.org/ |
Conference
| Conference | IEEE International Conference on Data Engineering 2018 |
|---|---|
| Abbreviated title | ICDE 2018 |
| Country | France |
| City | Paris |
| Period | 16/04/18 → 19/04/18 |
| Internet address |
Keywords
- Cross domain
- Social network
- User linkage
Cite this
}
Effective and efficient user account linkage across location based social networks. / Chen, Wei; Yin, Hongzhi; Wang, Weiqing; Zhao, Lei; Zhou, Xiaofang.
Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018: 16–19 April 2018 Paris, France. ed. / Panos K. Chrysanthis; Jens Dittrich; Beng Chin Ooi. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1085-1096 8509322.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Effective and efficient user account linkage across location based social networks
AU - Chen, Wei
AU - Yin, Hongzhi
AU - Wang, Weiqing
AU - Zhao, Lei
AU - Zhou, Xiaofang
PY - 2018
Y1 - 2018
N2 - Sources of complementary information are connected when we link the user accounts belonging to the same user across different domains or devices. The expanded information promotes the development of a wide range of applications, such as cross-domain prediction, cross-domain recommendation, and advertisement. Due to the great significance of user account linkage, there are increasing research works on this study. With the widespread popularization of GPS-enabled mobile devices, linking user accounts with location data has become an important and promising research topic. Being different from most existing studies in this domain that only focus on the effectiveness, we propose novel approaches to improve both effectiveness and efficiency of user account linkage. In this paper, a kernel density estimation (KDE) based method has been proposed to improve the accuracy by alleviating the data sparsity problem in measuring users' similarities. To improve the efficiency, we develop a grid-based structure to organize location data to prune the search space. The extensive experiments conducted on two real-world datasets demonstrate the superiority of the proposed approach in terms of both effectiveness and efficiency compared with the state-of-Art methods.
AB - Sources of complementary information are connected when we link the user accounts belonging to the same user across different domains or devices. The expanded information promotes the development of a wide range of applications, such as cross-domain prediction, cross-domain recommendation, and advertisement. Due to the great significance of user account linkage, there are increasing research works on this study. With the widespread popularization of GPS-enabled mobile devices, linking user accounts with location data has become an important and promising research topic. Being different from most existing studies in this domain that only focus on the effectiveness, we propose novel approaches to improve both effectiveness and efficiency of user account linkage. In this paper, a kernel density estimation (KDE) based method has been proposed to improve the accuracy by alleviating the data sparsity problem in measuring users' similarities. To improve the efficiency, we develop a grid-based structure to organize location data to prune the search space. The extensive experiments conducted on two real-world datasets demonstrate the superiority of the proposed approach in terms of both effectiveness and efficiency compared with the state-of-Art methods.
KW - Cross domain
KW - Social network
KW - User linkage
UR - http://www.scopus.com/inward/record.url?scp=85057125125&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00101
DO - 10.1109/ICDE.2018.00101
M3 - Conference Paper
SN - 9781538655214
SP - 1085
EP - 1096
BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
A2 - K. Chrysanthis, Panos
A2 - Dittrich, Jens
A2 - Chin Ooi, Beng
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -