Delivery Time Prediction Using Large-Scale Graph Structure Learning Based on Quantile Regression

Abstract

Predicting Estimated Time of Arrival (ETA) for packages is a critical problem in e-commerce. The prediction is often made based on spatial (sending and receiving addresses), temporal (payment time), and context (merchants) attributes. Existing methods usually formalize this task as an Origin-Destination (OD) ETA prediction problem and exploit the attribute relations with graph learning. However, most existing methods make use of fixed and manually defined graph structures, which are often not optimal for downstream ETA task and hence lead to unsatisfactory prediction results. In addition, current ETA models tend to focus on prediction accuracy without considering fulfillment rate. This may lead to a low fulfillment rate in practice, i.e., actual delivery time is much longer than estimations provided by models, which consequently exacerbates the frustrating experiences for users. To address these issues, we propose a novel Graph Structure Learning-based Quantile Regression (GSL-QR) model for e-commerce ETA prediction in this paper. Specifically, we utilize graph structure learning to dynamically update the spatial and temporal relation graphs of orders and learn optimal graph structures and graph embeddings guided by downstream ETA prediction task. To guarantee both prediction accuracy and order fulfillment rate, we design a multi-objective quantile regression in GSL-QR that can find the Pareto solution of the problem. In order to extend GSL to large-scale real-world graphs, we devise a Fast Sampling-based Graph Structure Learning (FS-GSL) method, which can significantly reduce the computational complexity of graph structure learning. Finally, we conduct comprehensive experiments on three industrial datasets collected from Alibaba e-commerce platform. The results demonstrate that the proposed model can significantly outperform baselines on both ETA prediction accuracy and order fulfillment rate.