Context-aware location annotation on mobility records through user grouping

Yong Zhang, Hua Wei, Xuelian Lin, Fei Wu, Zhenhui Li, Kaiheng Chen, Yuandong Wang, Jie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the increasing popularity of location-based services, a massive volume of human mobility records have been generated. At the same time, the growing spatial context data provides us rich semantic information. Associating the mobility records with relevant surrounding contexts, known as the location annotation, enables us to understand the semantics of the mobility records and helps further tasks like advertising. However, the location annotation problem is challenging due to the ambiguity of contexts and the sparsity of personal data. To solve this problem, we propose a Context-Aware location annotation method through User Grouping (CAUG) to annotate locations with venues. This method leverages user grouping and venue categories to alleviate the data sparsity issue and annotates locations according to multi-view information (spatial, temporal and contextual) of multiple granularities. Through extensive experiments on a real-world dataset, we demonstrate that our method significantly outperforms other baseline methods.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
PublisherSpringer Verlag
Pages583-596
Number of pages14
ISBN (Print)9783319930398
DOIs
StatePublished - Jan 1 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: Jun 3 2018Jun 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
CountryAustralia
CityMelbourne
Period6/3/186/6/18

Fingerprint

Context-aware
Grouping
Annotation
Sparsity
Semantics
Location based services
Data privacy
Spatial Information
Marketing
Granularity
Leverage
Baseline
Demonstrate
Experiments
Experiment
Context

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, Y., Wei, H., Lin, X., Wu, F., Li, Z., Chen, K., ... Xu, J. (2018). Context-aware location annotation on mobility records through user grouping. In G. I. Webb, D. Phung, M. Ganji, L. Rashidi, V. S. Tseng, & B. Ho (Eds.), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings (pp. 583-596). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10939 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93040-4_46
Zhang, Yong ; Wei, Hua ; Lin, Xuelian ; Wu, Fei ; Li, Zhenhui ; Chen, Kaiheng ; Wang, Yuandong ; Xu, Jie. / Context-aware location annotation on mobility records through user grouping. Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. editor / Geoffrey I. Webb ; Dinh Phung ; Mohadeseh Ganji ; Lida Rashidi ; Vincent S. Tseng ; Bao Ho. Springer Verlag, 2018. pp. 583-596 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{732f43ade6c647838b9dd6ed4b11f1f9,
title = "Context-aware location annotation on mobility records through user grouping",
abstract = "Due to the increasing popularity of location-based services, a massive volume of human mobility records have been generated. At the same time, the growing spatial context data provides us rich semantic information. Associating the mobility records with relevant surrounding contexts, known as the location annotation, enables us to understand the semantics of the mobility records and helps further tasks like advertising. However, the location annotation problem is challenging due to the ambiguity of contexts and the sparsity of personal data. To solve this problem, we propose a Context-Aware location annotation method through User Grouping (CAUG) to annotate locations with venues. This method leverages user grouping and venue categories to alleviate the data sparsity issue and annotates locations according to multi-view information (spatial, temporal and contextual) of multiple granularities. Through extensive experiments on a real-world dataset, we demonstrate that our method significantly outperforms other baseline methods.",
author = "Yong Zhang and Hua Wei and Xuelian Lin and Fei Wu and Zhenhui Li and Kaiheng Chen and Yuandong Wang and Jie Xu",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-93040-4_46",
language = "English (US)",
isbn = "9783319930398",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "583--596",
editor = "Webb, {Geoffrey I.} and Dinh Phung and Mohadeseh Ganji and Lida Rashidi and Tseng, {Vincent S.} and Bao Ho",
booktitle = "Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings",
address = "Germany",

}

Zhang, Y, Wei, H, Lin, X, Wu, F, Li, Z, Chen, K, Wang, Y & Xu, J 2018, Context-aware location annotation on mobility records through user grouping. in GI Webb, D Phung, M Ganji, L Rashidi, VS Tseng & B Ho (eds), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10939 LNAI, Springer Verlag, pp. 583-596, 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 6/3/18. https://doi.org/10.1007/978-3-319-93040-4_46

Context-aware location annotation on mobility records through user grouping. / Zhang, Yong; Wei, Hua; Lin, Xuelian; Wu, Fei; Li, Zhenhui; Chen, Kaiheng; Wang, Yuandong; Xu, Jie.

Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. ed. / Geoffrey I. Webb; Dinh Phung; Mohadeseh Ganji; Lida Rashidi; Vincent S. Tseng; Bao Ho. Springer Verlag, 2018. p. 583-596 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10939 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Context-aware location annotation on mobility records through user grouping

AU - Zhang, Yong

AU - Wei, Hua

AU - Lin, Xuelian

AU - Wu, Fei

AU - Li, Zhenhui

AU - Chen, Kaiheng

AU - Wang, Yuandong

AU - Xu, Jie

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Due to the increasing popularity of location-based services, a massive volume of human mobility records have been generated. At the same time, the growing spatial context data provides us rich semantic information. Associating the mobility records with relevant surrounding contexts, known as the location annotation, enables us to understand the semantics of the mobility records and helps further tasks like advertising. However, the location annotation problem is challenging due to the ambiguity of contexts and the sparsity of personal data. To solve this problem, we propose a Context-Aware location annotation method through User Grouping (CAUG) to annotate locations with venues. This method leverages user grouping and venue categories to alleviate the data sparsity issue and annotates locations according to multi-view information (spatial, temporal and contextual) of multiple granularities. Through extensive experiments on a real-world dataset, we demonstrate that our method significantly outperforms other baseline methods.

AB - Due to the increasing popularity of location-based services, a massive volume of human mobility records have been generated. At the same time, the growing spatial context data provides us rich semantic information. Associating the mobility records with relevant surrounding contexts, known as the location annotation, enables us to understand the semantics of the mobility records and helps further tasks like advertising. However, the location annotation problem is challenging due to the ambiguity of contexts and the sparsity of personal data. To solve this problem, we propose a Context-Aware location annotation method through User Grouping (CAUG) to annotate locations with venues. This method leverages user grouping and venue categories to alleviate the data sparsity issue and annotates locations according to multi-view information (spatial, temporal and contextual) of multiple granularities. Through extensive experiments on a real-world dataset, we demonstrate that our method significantly outperforms other baseline methods.

UR - http://www.scopus.com/inward/record.url?scp=85049381286&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049381286&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93040-4_46

DO - 10.1007/978-3-319-93040-4_46

M3 - Conference contribution

AN - SCOPUS:85049381286

SN - 9783319930398

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 583

EP - 596

BT - Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings

A2 - Webb, Geoffrey I.

A2 - Phung, Dinh

A2 - Ganji, Mohadeseh

A2 - Rashidi, Lida

A2 - Tseng, Vincent S.

A2 - Ho, Bao

PB - Springer Verlag

ER -

Zhang Y, Wei H, Lin X, Wu F, Li Z, Chen K et al. Context-aware location annotation on mobility records through user grouping. In Webb GI, Phung D, Ganji M, Rashidi L, Tseng VS, Ho B, editors, Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Springer Verlag. 2018. p. 583-596. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93040-4_46