Effective and efficient user account linkage across location based social networks

Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Xiaofang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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 languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Subtitle of host publication16–19 April 2018 Paris, France
EditorsPanos K. Chrysanthis, Jens Dittrich, Beng Chin Ooi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1085-1096
Number of pages12
ISBN (Electronic)9781538655207
ISBN (Print)9781538655214
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Data Engineering 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018
Conference number: 34th
https://icde2018.org/

Conference

ConferenceIEEE International Conference on Data Engineering 2018
Abbreviated titleICDE 2018
CountryFrance
CityParis
Period16/04/1819/04/18
Internet address

Keywords

  • Cross domain
  • Social network
  • User linkage

Cite this

Chen, W., Yin, H., Wang, W., Zhao, L., & Zhou, X. (2018). Effective and efficient user account linkage across location based social networks. In P. K. Chrysanthis, J. Dittrich, & B. Chin Ooi (Eds.), Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018: 16–19 April 2018 Paris, France (pp. 1085-1096). [8509322] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2018.00101
Chen, Wei ; Yin, Hongzhi ; Wang, Weiqing ; Zhao, Lei ; Zhou, Xiaofang. / Effective and efficient user account linkage across location based social networks. Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018: 16–19 April 2018 Paris, France. editor / Panos K. Chrysanthis ; Jens Dittrich ; Beng Chin Ooi. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 1085-1096
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title = "Effective and efficient user account linkage across location based social networks",
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.",
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Chen, W, Yin, H, Wang, W, Zhao, L & Zhou, X 2018, Effective and efficient user account linkage across location based social networks. in P K. Chrysanthis, J Dittrich & B Chin Ooi (eds), Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018: 16–19 April 2018 Paris, France., 8509322, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1085-1096, IEEE International Conference on Data Engineering 2018, Paris, France, 16/04/18. https://doi.org/10.1109/ICDE.2018.00101

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 proceedingConference PaperResearchpeer-review

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AU - Yin, Hongzhi

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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.

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Chen W, Yin H, Wang W, Zhao L, Zhou X. Effective and efficient user account linkage across location based social networks. In K. Chrysanthis P, Dittrich J, Chin Ooi B, editors, Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018: 16–19 April 2018 Paris, France. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 1085-1096. 8509322 https://doi.org/10.1109/ICDE.2018.00101