针对商户识别中的线上到线下物流业务的可信半监督异常检测

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yong Li, Shuhang Wang, Shijie Xu, Jiao Yin
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引用次数: 0

摘要

在线到离线(O2O)电子商务业务的兴起为物流业带来了巨大商机。在 "线上到线下 "的物流业务中,必须及时发现有欺诈发货行为的异常商户,如以低折扣发送其他商户的包裹牟利。这有助于减少平台的经济损失,确保健康的环境。现有的异常检测研究主要集中于在线欺诈行为检测,如电子商务中的欺诈性购买和评论行为。然而,由于物流中包裹发送行为的线上和线下操作较为复杂,且线下部署对可解释性有一定要求,因此这些方法并不适合物流中的异常商家检测。MultiDet 是一种基于半监督多视角融合的线上到线下物流异常检测框架,由基本版 SemiDet 和注意力增强型多视角融合模型组成。在 SemiDet 中,首先要进行成对数据增强,以提高模型的鲁棒性,并应对有限标签异常实例的挑战。然后,SemiDet 利用自动编码器框架计算每个商家的异常评分。考虑到物流商户之间的多重关系,进一步设计了基于多视角注意力融合的异常检测网络,以捕捉商户之间的相互影响,提高异常商户的检测性能。设计了基于事后扰动的解释模型,以输出不同视图的重要性,确保端到端异常检测的可信度。该框架基于从中国最大的物流平台之一收集的为期八个月的真实数据集进行评估,涉及北京地区的 6128 个商家和 1600 万条历史订单发货人记录。实验结果表明,所提出的模型在 AUC-ROC 和 AUC-PR 指标上都优于其他基线模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Trustworthy semi-supervised anomaly detection for online-to-offline logistics business in merchant identification

Trustworthy semi-supervised anomaly detection for online-to-offline logistics business in merchant identification

The rise of online-to-offline (O2O) e-commerce business has brought tremendous opportunities to the logistics industry. In the online-to-offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e-commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package-sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi-supervised multi-view fusion-based Anomaly Detection framework in online-to-offline logistics is proposed, which consists of a basic version SemiDet and an attention-enhanced multi-view fusion model. In SemiDet, pair-wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto-encoder framework. Considering the multi-relationships among logistics merchants, a multi-view attention fusion-based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post-hoc perturbation-based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end-to-end anomaly detection. The framework based on an eight-month real-world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC-ROC and AUC-PR metrics.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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