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Predicting interactions and contexts with context trees

Published:31 October 2016Publication History

ABSTRACT

Predicting the future actions of individuals from geospatial data has the potential to provide a basis for tailored services. This work presents the Predictive Context Tree (PCT), a new hierarchical classifier based on the Context Tree summary model [8]. The PCT is capable of predicting the future contexts and locations of individuals to provide a basis for understanding not only where a user will be, but also what type of activity they will be performing. Through a comparison to established techniques, this paper demonstrates the applicability of the PCT by showing increased accuracies for location prediction, and increased utility through context prediction.

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      • Published in

        cover image ACM Other conferences
        SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
        October 2016
        649 pages
        ISBN:9781450345897
        DOI:10.1145/2996913

        Copyright © 2016 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 31 October 2016

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        SIGSPACIAL '16 Paper Acceptance Rate40of216submissions,19%Overall Acceptance Rate220of1,116submissions,20%
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