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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22733

Title: An approach to compute user similarity for GPS applications
Authors: Mazumdar, Pramit
Patra, Bidyut
Lock, Russell
Korra, Sathya Babu
Keywords: GPS data
User similarity
Semantic location
Trajectory pattern mining
Issue Date: 2016
Publisher: © Elsevier
Citation: MAZUMDAR, P. ... et al, 2016. An approach to compute user similarity for GPS applications. Knowledge-Based Systems, doi: 10.1016/j.knosys.2016.09.017.
Abstract: 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.
Description: This paper is closed access until 28th September 2017.
Version: Accepted for publication
DOI: 10.1016/j.knosys.2016.09.017
URI: https://dspace.lboro.ac.uk/2134/22733
Publisher Link: http://dx.doi.org/10.1016/j.knosys.2016.09.017
ISSN: 0950-7051
Appears in Collections:Closed Access (Computer Science)

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