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Probabilistic identification of visited point-of-interest for personalized automatic check-in

Authors:

Kyosuke Nishida,

Takeshi Kurashima,

Yoshihiko SuharaAuthors Info & Claims

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 631 - 642

https://doi.org/10.1145/2632048.2632092

Published: 13 September 2014 Publication History

Abstract

Automatic check-in, which is to identify a user's visited points of interest (POIs) from his or her trajectories, is still an open problem because of positioning errors and the high POI density in small areas. In this study, we propose a probabilistic visited-POI identification method. The method uses a new hierarchical Bayesian model for identifying the latent visited-POI label of stay points, which are automatically extracted from trajectories. This model learns from labeled and unlabeled stay point data (i.e., semi-supervised learning) and takes into account personal preferences, stay locations including positioning errors, stay times for each category, and prior knowledge about typical user preferences and stay times. Experimental results with real user trajectories and POIs of Foursquare demonstrated that our method achieved statistically significant improvements in precision at 1 and recall at 3 over the nearest neighbor method and a conventional method that uses a supervised learning-to-rank algorithm.

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Cited By

Gao QFu HZhang KTrajcevski GTeng XZhou F(2023)Inferring Real Mobility in Presence of Fake Check-ins DataACM Transactions on Intelligent Systems and Technology10.1145/3604941Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3604941
Tong WTong YXia CHua JLi QZhong S(2022)Understanding Location Privacy of the Point-of-Interest Aggregate Data via Practical Attacks and DefensesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.3184279(1-17)Online publication date: 2022
https://doi.org/10.1109/TDSC.2022.3184279
Toda TMoriwaki DOta K(2022)Aggregate Learning for Mixed Frequency Data2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020900(4157-4165)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020900
Show More Cited By

Index Terms

Probabilistic identification of visited point-of-interest for personalized automatic check-in
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
  2. World Wide Web
    1. Web applications
    2. Web services

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Graphical abstract

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Abstract
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Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2014

973 pages

ISBN:9781450329682

DOI:10.1145/2632048

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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New York, NY, United States

Publication History

Published: 13 September 2014

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UbiComp '14

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Gao QFu HZhang KTrajcevski GTeng XZhou F(2023)Inferring Real Mobility in Presence of Fake Check-ins DataACM Transactions on Intelligent Systems and Technology10.1145/3604941Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3604941
Tong WTong YXia CHua JLi QZhong S(2022)Understanding Location Privacy of the Point-of-Interest Aggregate Data via Practical Attacks and DefensesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.3184279(1-17)Online publication date: 2022
https://doi.org/10.1109/TDSC.2022.3184279
Toda TMoriwaki DOta K(2022)Aggregate Learning for Mixed Frequency Data2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020900(4157-4165)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020900
Tong WXia CHua JLi QZhong S(2021)Practical Location Privacy Attacks and Defense on Point-of-interest Aggregates2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS51616.2021.00082(808-818)Online publication date: Jul-2021
https://doi.org/10.1109/ICDCS51616.2021.00082
Yuan ZLiu HLiu YZhang DYi FZhu NXiong HHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Spatio-Temporal Dual Graph Attention Network for Query-POI MatchingProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401159(629-638)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401159
Lu XYu ZLiu CLiu YXiong HGuo B(2020)Inferring Lifetime Status of Point-of-InterestACM Transactions on Knowledge Discovery from Data10.1145/336979914:1(1-27)Online publication date: 3-Feb-2020
https://dl.acm.org/doi/10.1145/3369799
De Paola AGiammanco ARe GMorana MDe Rango FCalafate CVoznak M(2019)Human mobility simulator for smart applicationsProceedings of the 23rd IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications10.5555/3395101.3395138(203-210)Online publication date: 7-Oct-2019
https://dl.acm.org/doi/10.5555/3395101.3395138
Yu DHu QWang S(2019)A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi PositioningISPRS International Journal of Geo-Information10.3390/ijgi81104688:11(468)Online publication date: 23-Oct-2019
https://doi.org/10.3390/ijgi8110468
IWATA SISHIKAWA KTAKAYAMA TYANAGISAWA MTOGAWA N(2019)A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured PositionsIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E102.A.860E102.A:6(860-865)Online publication date: 1-Jun-2019
https://doi.org/10.1587/transfun.E102.A.860
de Paola AGiammanco Alo Re GAnastasi G(2019)Detection of Points of Interest in a Smart Campus2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)10.1109/RTSI.2019.8895569(155-160)Online publication date: Sep-2019
https://doi.org/10.1109/RTSI.2019.8895569
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