Fed-SCRP: Federated Multi-View Learning for Seller Claim Risk Prediction in Logistics Scenarios
Yao Lu, Shuai Wang, Hai Wang, Xiaohui Zhao, Shuai Wang, Xiaolei Zhou, Wei Gong · 2024
The emergence of e-commerce with logistics service provides great convenience to people’s lives. However, platform usually receive seller claim for some reasons (e.g., damaged packages). Thus, it is important to predict seller claim risk in logistics scenarios. Existing solution for seller claim risk predict are challenging to address this problem due to two unique features including (i) multi-side collaborative risk factor caused by data sharing constraints of e-commerce and logistics platform, (ii) industry-specific risk factor caused by the dynamic similarity of sellers. To incorporate these new factors, we propose a novel seller claim risk prediction framework (Fed-SCRP) via federated multi-view learning, where we (i) design a federated multi-view learning to deal with data isolation problem, (ii) develop a STG-SRIM model and a series of information union transformers to capture the hybrid semantic embedding of dynamic seller features and industry-specific risk factor. We conduct a comprehensive evaluation of our method using datasets from major Chinese e-commerce and logistics platforms. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.