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Learning Location Correlation from GPS Trajectories

Published: 23 May 2010 Publication History

Abstract

People’s location histories imply the location correlation that states the relations between geographical locations in the space of human behavior. With the correlation, we can enable many valuable services, such as location recommendation and sales promotion. In this paper, by taking into account a user’s travel experience (knowledge) and the sequentiality that locations have been visited, we learn the location correlation from a large number of user-generated GPS trajectories. Using the location correlation, we conduct a personalized location recommendation system, which is evaluated based on a real-world GPS dataset collected by 112 users over a period of 1.5 years. As a result, our method outperforms that using the Pearson correlation.

Cited By

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  • (2023)SAInf: Stay Area Inference of Vehicles using Surveillance Camera RecordsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599952(4595-4604)Online publication date: 6-Aug-2023
  • (2021)EMD-Based Semantic User Similarity Using Past Travel HistoriesJournal of Cases on Information Technology10.4018/JCIT.20220701.oa224:3(1-17)Online publication date: 8-Oct-2021
  • (2019)Clustering Users by Their Mobility Behavioral PatternsACM Transactions on Knowledge Discovery from Data10.1145/332212613:4(1-28)Online publication date: 20-Aug-2019
  • Show More Cited By

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Published In

cover image Guide Proceedings
MDM '10: Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
May 2010
434 pages
ISBN:9780769540481

Publisher

IEEE Computer Society

United States

Publication History

Published: 23 May 2010

Author Tags

  1. GPS trajectory
  2. Location Correlation
  3. Location History
  4. Spatial Data Mining

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Cited By

View all
  • (2023)SAInf: Stay Area Inference of Vehicles using Surveillance Camera RecordsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599952(4595-4604)Online publication date: 6-Aug-2023
  • (2021)EMD-Based Semantic User Similarity Using Past Travel HistoriesJournal of Cases on Information Technology10.4018/JCIT.20220701.oa224:3(1-17)Online publication date: 8-Oct-2021
  • (2019)Clustering Users by Their Mobility Behavioral PatternsACM Transactions on Knowledge Discovery from Data10.1145/332212613:4(1-28)Online publication date: 20-Aug-2019
  • (2018)Mining Spatial-Temporal Travel Patterns from Highway Transaction DataProceedings of the 2nd International Symposium on Computer Science and Intelligent Control10.1145/3284557.3284565(1-7)Online publication date: 21-Sep-2018
  • (2018)CO2ACM Transactions on Information Systems10.1145/318216436:3(1-29)Online publication date: 13-Mar-2018
  • (2018)Analysis of passenger travel behavior based on public transportation OD dataProceedings of the 6th International Conference on Information and Education Technology10.1145/3178158.3178207(275-280)Online publication date: 6-Jan-2018
  • (2018)SenseDcityProceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking10.1145/3170521.3170537(1-6)Online publication date: 4-Jan-2018
  • (2017)Activity Recommendation with PartnersACM Transactions on the Web10.1145/312140712:1(1-29)Online publication date: 9-Sep-2017
  • (2017)Location-Based Distance Measures for Geosocial SimilarityACM Transactions on the Web10.1145/305495111:3(1-32)Online publication date: 3-Jul-2017
  • (2017)Semantic-aware Query Processing for Activity TrajectoriesProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018678(283-292)Online publication date: 2-Feb-2017
  • Show More Cited By

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