Inductive Graph Transformer for Delivery Time Estimation

TL;DR

This paper introduces an inductive graph transformer model that effectively predicts package delivery times for unseen retailers by leveraging raw features and simplified graph neural networks, outperforming existing methods.

Contribution

The paper proposes a novel inductive graph transformer with a decoupled pipeline and simplified GNN for large-scale delivery time estimation, addressing inductive inference and high-order interactions.

Findings

Significantly outperforms state-of-the-art methods on real-world datasets.

Effectively handles inductive inference for unseen retailers.

Simplified GNN structure reduces complexity and enables industrial scalability.

Abstract

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inference. Models are required to predict ETA for orders with unseen retailers and addresses; 2) High-order interaction of order semantic information. Apart from the spatio-temporal features, the estimated time also varies greatly with other factors, such as the packaging efficiency of retailers, as well as the high-order interaction of these factors. In this paper, we propose an inductive graph transformer (IGT) that leverages raw feature information and structural graph data to estimate package delivery time. Different from previous graph transformer architectures, IGT adopts a decoupled pipeline and trains transformer as a regression function that can capture the multiplex information from both raw feature and dense embeddings encoded by a graph neural network (GNN). In addition, we further simplify the GNN structure by removing its non-linear activation and the learnable linear transformation matrix. The reduced parameter search space and linear information propagation in the simplified GNN enable the IGT to be applied in large-scale industrial scenarios. Experiments on real-world logistics datasets show that our proposed model can significantly outperform the state-of-the-art methods on estimation of delivery time. The source code is available at: https://github.com/enoche/IGT-WSDM23.