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Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12978))

Abstract

Amazon Last Mile strives to learn an accurate delivery point for each address by using the noisy GPS locations reported from past deliveries. Centroids and other center-finding methods do not serve well, because the noise is consistently biased. The problem calls for supervised machine learning, but how? We addressed it with a novel adaptation of learning to rank from the information retrieval domain. This also enabled information fusion from map layers. Offline experiments show outstanding reduction in error distance, and online experiments estimated millions in annualized savings.

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Notes

  1. 1.

    Before there is any delivery history for an address, the process is bootstrapped by other approximate geocoding methods to guide the driver. Third-party geocodes are not used in our geocode computations.

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Author information

Authors and Affiliations

  1. Amazon, Bellevue, WA, USA

    George Forman

Authors
  1. George Forman
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Correspondence to George Forman .

Editor information

Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Forman, G. (2021). Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12978. Springer, Cham. https://doi.org/10.1007/978-3-030-86514-6_25

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  • DOI: https://doi.org/10.1007/978-3-030-86514-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86513-9

  • Online ISBN: 978-3-030-86514-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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