Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation
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.
Cite
Text
Forman. "Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86514-6_25Markdown
[Forman. "Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/forman2021ecmlpkdd-getting/) doi:10.1007/978-3-030-86514-6_25BibTeX
@inproceedings{forman2021ecmlpkdd-getting,
title = {{Getting Your Package to the Right Place: Supervised Machine Learning for Geolocation}},
author = {Forman, George},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2021},
pages = {403-419},
doi = {10.1007/978-3-030-86514-6_25},
url = {https://mlanthology.org/ecmlpkdd/2021/forman2021ecmlpkdd-getting/}
}