ML Anthology

Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application

Mingxuan Yue, Tianshu Sun, Fan Wu, Lixia Wu, Yinghui Xu, Cyrus Shahabi
ECML-PKDD 2020 pp. 52-68
doi:10.1007/978-3-030-67667-4_4 /ecmlpkdd/2020/yue2020ecmlpkdd-learning/

Abstract

A good representation of urban areas is of great importance in on-demand delivery services such as for ETA prediction. However, the existing representations learn either from sparse check-in histories or topological geometries, thus are either lacking coverage and violating the geographical law or ignoring contextual information from data. In this paper, we propose a novel representation learning framework for obtaining a unified representation of Area of Interest from both contextual data (trajectories) and topological data (graphs). The framework first encodes trajectories and graphs into homogeneous views, and then train a multi-view autoencoder to learn the representation of areas using a ranking-based loss. Experiments with real-world package delivery data on ETA prediction confirm the effectiveness of the model.

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Cite

Text

Yue et al. "Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67667-4_4

Markdown

[Yue et al. "Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/yue2020ecmlpkdd-learning/) doi:10.1007/978-3-030-67667-4_4

BibTeX

@inproceedings{yue2020ecmlpkdd-learning,
  title     = {{Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application}},
  author    = {Yue, Mingxuan and Sun, Tianshu and Wu, Fan and Wu, Lixia and Xu, Yinghui and Shahabi, Cyrus},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2020},
  pages     = {52-68},
  doi       = {10.1007/978-3-030-67667-4_4},
  url       = {https://mlanthology.org/ecmlpkdd/2020/yue2020ecmlpkdd-learning/}
}