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.
翻译:提供用户购买电子商务平台的网页的包件交付的准确估计时间对于其采购决定和购买后经验非常重要。虽然这一问题与常规估计抵达时间(ETA)有着一些共同的问题,但在以下方面更具挑战性:1) 诱导性推断,模型需要预测ETA与隐形零售商和地址的订单;2) 秩序语义信息的高度互动。除了简易时空特征外,估计时间与其他因素也有很大差异,例如零售商的包装效率以及这些因素的高度顺序互动。在本文件中,我们建议使用一个缩影图变换器(IGT),利用原始地物信息和结构图形数据来估计包装交付时间。不同于以前的图形变异器结构,IGT采用分解式管道,将变异器作为回归功能,从原始特性和由图表源代码(GNNNN)编码的密集嵌入中获取多种信息。此外,我们还进一步简化了GNNNF结构结构,在非线性地实时交付时间模型中取消了其原始特性信息和结构图示式图表,在简化的I-SDMFS-S-S-S-S-S-S-S-Silnial-Siral-S-S-Sild-Silview-S-I-I-I-I-Sild-S-S-Sild-S-S-S-S-I-S-S-S-S-S-I-S-S-S-S-S-S-S-S-S-S-S-I-I-S-S-I-I-I-I-I-I-I-I-S-S-S-I-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-I-I-I-I-I-I-I-I-I-I-I-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-