2020/04/20 by Yongchun Zhu, Dongbo Xi, Bowen Song +4
Computer Science · Mathematics · #Computer science #Data Mining Algorithms and Applications #Domain (mathematical analysis) #Imbalanced Data Classification Techniques #Mathematics #Spam and Phishing Detection #cs.AI #cs.IR #cs.LG
paper · pdf · doi:10.1145/3366423.3380172
TheWebConf(WWW) 2020 Main Conference Long Paper
openalex publication_date 2020/04/20 · arxiv created 2022/01/04 · arxiv updated 2022/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
With the explosive growth of the e-commerce industry, detecting online transaction fraud in real-world applications has become increasingly important to the development of e-commerce platforms. The sequential behavior history of users provides useful information in differentiating fraudulent payments from regular ones. Recently, some approaches have been proposed to solve this sequence-based fraud detection problem. However, these methods usually suffer from two problems: the prediction results are difficult to explain and the exploitation of the internal information of behaviors is insufficient. To tackle the above two problems, we propose a Hierarchical Explainable Network (HEN) to model users’ behavior sequences, which could not only improve the performance of fraud detection but also make the inference process interpretable.