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An approach to compute user similarity for GPS applications

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journal contribution
posted on 07.10.2016, 13:18 authored by Pramit Mazumdar, Bidyut Patra, Russell LockRussell Lock, Sathya Babu Korra
The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K-nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K-neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature.

History

School

  • Science

Department

  • Computer Science

Published in

Knowledge-Based Systems

Volume

113

Pages

125 - 142

Citation

MAZUMDAR, P. ... et al, 2016. An approach to compute user similarity for GPS applications. Knowledge-Based Systems, 113, pp. 125-142.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

20/09/2016

Publication date

2016-09-28

Notes

This paper was accepted for publication in the journal Knowledge-Based Systems and the definitive published version is available at http://dx.doi.org/10.1016/j.knosys.2016.09.017.

ISSN

0950-7051

Language

en