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HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce

Authors:

Guang WangAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 3402 - 3411

https://doi.org/10.1145/3583780.3614918

Published: 21 October 2023 Publication History

Abstract

Warehouse-distribution integration has been adopted by many e-commerce retailers (e.g., Amazon, TAOBAO, and JD) as an efficient business mode. In warehouse-distribution integration e-commerce, one of the most important problems is to estimate the full-link delivery time for better decision-making. Existing solutions for traditional warehouse-distribution separation mode are challenging to address this problem due to two unique features in the integration mode including (i) contextual influence caused by neighbor units in heterogeneous delivery networks, (ii) uncertain delivery time caused by the dynamic temporal data (e.g., online sales volume) and heterogeneity of delivery units. To incorporate these new factors, we propose Heterogeneous Spatial-Temporal Graph Transformer (HST-GT), a novel full-link delivery time estimation method under the warehouse-distribution integration mode, where we (i) develop heterogeneous graph transformers to capture hierarchical heterogeneous information; and (ii) design a set of spatial-temporal transformers based on heterogeneous features to fully exploit the correlation of spatial and temporal information. We extensively evaluate our method based on one-month real-world data consisting of hundreds of warehouses and sorting centers, and millions of historical orders collected from one of the largest e-commerce retailers in the world. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.

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Cited By

Zhou XWang WBuntine WQu SSriramulu ATan WBergmeir CSerra ESpezzano F(2024)Scalable Transformer for High Dimensional Multivariate Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679757(3515-3526)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679757
Zhao YXi WBai GLiu XZhao J(2024)Heterogeneous Interactive Graph Network for Audio–Visual Question AnsweringKnowledge-Based Systems10.1016/j.knosys.2024.112165300(112165)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112165

Index Terms

HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

Copyright © 2023 ACM.

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Published: 21 October 2023

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2030 major projects of scientific and technological innovation

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Cited By

Zhou XWang WBuntine WQu SSriramulu ATan WBergmeir CSerra ESpezzano F(2024)Scalable Transformer for High Dimensional Multivariate Time Series ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679757(3515-3526)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679757
Zhao YXi WBai GLiu XZhao J(2024)Heterogeneous Interactive Graph Network for Audio–Visual Question AnsweringKnowledge-Based Systems10.1016/j.knosys.2024.112165300(112165)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112165

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