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

Risk item identification is vital for protecting the health of e-commerce 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. This paper proposes a novel Clustering-based Supervised Contrastive Learning (CSCL) to address the two challenges. CSCL first devises a contrastive heterogeneous graph neural network, which 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.