Clustering-Based Supervised Contrastive Learning for Identifying Risk Items on Heterogeneous Graph

Abstract

Risk item identification is vital for protecting the health of ecommerce trades. Existing solutions prefer to model structure information besides item attributes and optimize parameters in cross-entropy (CE) manners. However, the few labeled and imbalanced supervision in real-world scenarios usually results in poor generalization of CE optimization. More seriously, the pattern-level difference of risk items is often neglected in binary supervised learning, leading to limited performance. In this paper, we propose a novel Clustering-based Supervised Contrastive Learning (CSCL) to address the two challenges. CSCL first devises a contrastive heterogeneous graph neural network that fully exploits multiple risk relations in contrastive learning, keeping generalization performance. It then designs a clustering-based reweighted sampling strategy to search informative positive and negative training instances for effective pattern-level optimization. We test the performance on Xianyu Platform, and experimental results demonstrate that CSCL outperforms all baselines.