Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation

نویسندگان

چکیده

Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving and payment time. Accurate DTE can boost platform revenue reduce customer complaints refunds. However, imbalanced nature industrial data impedes previous models from reaching satisfactory prediction performance. Although regression methods be applied to task, we experimentally find they improve performance low-shot samples at sacrifice overall To address issue, propose novel Dual Graph Multitask framework for (DGM-DTE). Our first classifies package as head tail data. Then, dual graph-based model utilized learn representations two categories In particular, DGM-DTE re-weights embedding by estimating its kernel density. We fuse capture both high- representations. Experiments real-world Taobao logistics datasets demonstrate superior compared baselines.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2023

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-30678-5_46