research-article

Survey on user location prediction based on geo-social networking data

Authors:

Zhixiao WangAuthors Info & Claims

World Wide Web, Volume 23, Issue 3

Pages 1621 - 1664

https://doi.org/10.1007/s11280-019-00777-8

Published: 01 May 2020 Publication History

Abstract

With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.

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Cited By

Zhao JXu JXu YFang JChao PZhou X(2024)CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337653936:9(4499-4514)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3376539
Tsanakas SHameed AViolos JLeivadeas A(2024)A light-weight edge-enabled knowledge distillation technique for next location prediction of multitude transportation meansFuture Generation Computer Systems10.1016/j.future.2023.12.025154:C(45-58)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.025
Pan XCai XSong KBaker TGadekallu TYuan X(2023)Location Recommendation Based on Mobility Graph With Individual and Group InfluencesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314986924:8(8409-8420)Online publication date: 1-Aug-2023
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Index Terms

Survey on user location prediction based on geo-social networking data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

Index terms have been assigned to the content through auto-classification.

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cover image World Wide Web

World Wide Web Volume 23, Issue 3

May 2020

735 pages

ISSN:1386-145X

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© Springer Science+Business Media, LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2020

Accepted: 23 December 2019

Revision received: 05 December 2019

Received: 29 April 2019

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Cited By

Zhao JXu JXu YFang JChao PZhou X(2024)CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337653936:9(4499-4514)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3376539
Tsanakas SHameed AViolos JLeivadeas A(2024)A light-weight edge-enabled knowledge distillation technique for next location prediction of multitude transportation meansFuture Generation Computer Systems10.1016/j.future.2023.12.025154:C(45-58)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.025
Pan XCai XSong KBaker TGadekallu TYuan X(2023)Location Recommendation Based on Mobility Graph With Individual and Group InfluencesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314986924:8(8409-8420)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2022.3149869
Liu YLuo XTao Z(2023)Construction of a high-precision general geographical location words datasetComputer Standards & Interfaces10.1016/j.csi.2022.10369284:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.csi.2022.103692
Wu JHu RLi DXiao YRen LHu W(2023)Where Have You Gone: Category-aware Multigraph Embedding for Missing Point-of-Interest IdentificationNeural Processing Letters10.1007/s11063-022-10996-255:3(3025-3044)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s11063-022-10996-2
Li XHu RWang Z(2023)Beyond fixed time and space: next POI recommendation via multi-grained context and correlationNeural Computing and Applications10.1007/s00521-022-07825-x35:1(907-920)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s00521-022-07825-x
Xu SXu JLi BFu X(2023)Predicting Where You Visit in a Surrounding City: A Mobility Knowledge Transfer Framework Based on Cross-City TravelersDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_22(334-350)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30637-2_22
Li Y(2022)A POI Recommendation Algorithm Based on the Heterogeneous Graph Convolution NetworkScientific Programming10.1155/2022/91547122022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9154712
Xu YXu JZhao JZheng KLiu AZhao LZhou XZhang ARangwala H(2022)MetaPTPProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539360(2151-2159)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539360
Zhu NCao JLu XLiu CLiu HLi YLuo XXiong H(2022)Predicting a Person’s Next Activity Region with a Dynamic Region-Relation-Aware Graph Neural NetworkACM Transactions on Knowledge Discovery from Data10.1145/352909116:6(1-23)Online publication date: 30-Jul-2022
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