DeepExpress：用于快递预测的异构耦合序列建模
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction
摘要：
快递配送序列的预测，即，建模和估计每日进出包裹的递送量对于在线业务、物流和积极的客户体验，特别是对于资源分配优化和促销活动安排至关重要。对消费者交付请求的精确估计必须涉及诸如购物行为、天气条件、事件、商业活动及其耦合等顺序因素。此外，传统的层序预测假设层序演化是稳定的，无法解决上述多源数据中复杂的非线性层序和各种特征效应。尽管深度网络和注意力机制展示了复杂序列建模的潜力，但现有网络忽略了特征和序列之间的异构和耦合情况，导致预测准确性较弱。为了解决这些问题，我们提出了DeepExpress -一种基于深度学习的快递序列预测模型，它扩展了经典的seq 2seq框架，以学习序列和特征之间的复杂耦合。DeepExpress利用快递seq 2seq学习，精心设计的异构特征表示，以及新颖的联合训练注意力机制来自适应地映射异构数据，并捕获序列-特征耦合以进行精确估计。实验结果表明，该方法优于浅基线和深基线模型。
英文摘要：
The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Besides, conventional sequence prediction assumes a stable sequence evolution, failing to address complex nonlinear sequences and various feature effects in the above multi-source data. Although deep networks and attention mechanisms demonstrate the potential of complex sequence modeling, extant networks ignore the heterogeneous and coupling situation between features and sequences, resulting in weak prediction accuracy. To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features. DeepExpress leverages an express delivery seq2seq learning, a carefully-designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively map heterogeneous data, and capture sequence-feature coupling for precise estimation. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
