A Local Algorithm for Product Return Prediction in E-Commerce
A Local Algorithm for Product Return Prediction in E-Commerce
复制标题
电子商务中产品退货预测的本地算法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Ajay A. Deshpande
中科院分区：
文献类型：
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作者：
Yada Zhu;Jianbo Li;Jingrui He;Brian Quanz;Ajay A. Deshpande
随着电子零售的快速发展，处理在线退货订单的成本也显著增加，成为电子商务行业面临的主要挑战。对产品退货的准确预测使电子零售商能够提前防止有问题的交易。然而，现有的客户在线购物行为建模和退货行为预测工作有限，未能整合产品购买和退货历史中的丰富信息（如退货历史、购买-不退货行为和客户/产品相似度）。此外，该问题涉及的大规模数据集通常由数百万客户和数万种产品组成，这也使得现有方法在预测产品退货方面效率低下。
With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns.
To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.