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Novelty Detection for Location Prediction Problems Using Boosting Trees

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10405))

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Abstract

Due to the enormous use of mobile applications and the wide spread of location-based services, as Foursquare, google maps, Facebook check-ins, it became a must to focus on studying these data and its impact on our social norms. In this paper, we are tackling the location novelty problem, which evaluates the user’s curiosity to explore new places. In order to maintain a better service and offer new services, such as recommending new places, optimizing marketing campaigns, we conducted these experiments to classify the next check-ins to be either Novel or regular. We can predict the novelty of the next Point of Interest (POI) up to 82%, by extracting different types of features, in space and time, and using boosting trees.

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Correspondence to Khaled Yasser .

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Yasser, K., Hemayed, E. (2017). Novelty Detection for Location Prediction Problems Using Boosting Trees. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-62395-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62394-8

  • Online ISBN: 978-3-319-62395-5

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