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Mining user similarity based on location history

Published: 05 November 2008 Publication History

Abstract

The pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enable people to conveniently log the location histories they visited with spatio-temporal data. The increasing availability of large amounts of spatio-temporal data pertaining to an individual's trajectories has given rise to a variety of geographic information systems, and also brings us opportunities and challenges to automatically discover valuable knowledge from these trajectories. In this paper, we move towards this direction and aim to geographically mine the similarity between users based on their location histories. Such user similarity is significant to individuals, communities and businesses by helping them effectively retrieve the information with high relevance. A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users. In this framework, we take into account both the sequence property of people's movement behaviors and the hierarchy property of geographic spaces. We evaluate this framework using the GPS data collected by 65 volunteers over a period of 6 months in the real world. As a result, HGSM outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures.

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cover image ACM Conferences
GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
November 2008
559 pages
ISBN:9781605583235
DOI:10.1145/1463434
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 ACM 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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Publication History

Published: 05 November 2008

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Author Tags

  1. GPS logs
  2. similar sequence matching
  3. spatio-temporal data mining
  4. user similarity

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2024)GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife DatasetProceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation10.1145/3681770.3698573(25-28)Online publication date: 29-Oct-2024
  • (2024)Large Language Models for Spatial Trajectory Patterns MiningProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698467(52-55)Online publication date: 29-Oct-2024
  • (2024)TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal ModelProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691303(362-371)Online publication date: 29-Oct-2024
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