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    Collective suffix tree-based models for location prediction

    Eldaw, Muawya Habib Sarnoub and Levene, Mark and Roussos, George (2013) Collective suffix tree-based models for location prediction. In: UNSPECIFIED (ed.) UbiComp '13: Adjunct Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. New York, U.S.: Association for Computing Machinery, pp. 441-450. ISBN 9781450322157.

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    Abstract

    Models developed for the prediction of location, where a specific individual will be present at a future time, are typically implemented using a one-model-per-user approach which cannot be employed for inferring collective or social behaviours involving other individuals. In this paper, we propose an alternative that allows for inference though a collaborative mechanism which does not require the profiling of individual users. This alternative utilises a suffix tree as its core underlying data structure, where predictions are computed over an aggregate record of behaviours of all users. We evaluate the performance of our model on the Nokia Mobile Data Collection Campaign data set and find that the collective approach performs well compared to individual user models. We also find that the commonly used Hit and Miss score on its own does not provide sufficient indication of prediction accuracy, and that employing additional metrics using the mean error may be preferable.

    Metadata

    Item Type: Book Section
    Additional Information: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Zurich, Switzerland — September 08 - 12, 2013
    Keyword(s) / Subject(s): User Mobility, Collective model, Suffix tree, Markov model, prediction of location
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Muawya Habib Sarnoub Eldaw
    Date Deposited: 05 Jun 2014 11:35
    Last Modified: 30 Jul 2019 01:18
    URI: http://eprints.bbk.ac.uk/id/eprint/9874

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