Prohibited Item Detection on Heterogeneous Risk Graphs

Abstract

Prohibited item detection, which aims to detect illegal items hidden on e-commerce platforms, plays a significant role in evading risks and preventing crimes for online shopping. While traditional solutions usually focus on mining evidence from independent items, they cannot effectively utilize the rich structural relevance among different items. A naive idea is to directly deploy existing supervised graph neural networks to learn node representations for item classification. However, the very few manually labeled items with various risk patterns introduce two essential challenges: (1) How to enhance the representations of enormous unlabeled items? (2) How to enrich the supervised information in this few-labeled but multiple-pattern business scenario? In this paper, we construct item logs as a Heterogeneous Risk Graph (HRG), and propose the novel Heterogeneous Self-supervised Prohibited item Detection model (HSPD) to overcome these challenges. HSPD first designs the heterogeneous self-supervised learning model, which treats multiple semantics as the supervision to enhance item representations. Then, it presents the directed pairwise labeling to learn the distance from candidates to their most relevant prohibited seeds, which tackles the binary-labeled multi-patterned risks. Finally, HSPD integrates with self-training mechanisms to iteratively expand confident pseudo labels for enriching supervision. The extensive offline and online experimental results on three real-world HRGs demonstrate that HSPD consistently outperforms the state-of-the-art alternatives.