We are the Governance & Experience Algorithm Team — a trailblazing AI force at the forefront of building a trusted, efficient e-commerce ecosystem. Established in 2020, we leverage Graph Neural Networks, Multi-Objective Optimization, Time Series Prediction, and Large Language Models (LLM) to tackle some of the most complex challenges in e-commerce:

- Build graph-powered risk networks to uncover similar product clusters and high-risk seller/creator groups in emerging markets, reducing false advertising incidents in global regions.

- Develop LLM-based multi-modal systems using cross-modal fusion (text, images, behavioral data) to detect false advertising and low-quality products. Deploy AI-powered product business suggestions to improve seller compliance and user trust.

- Lead time-series forecasting innovation for logistics performance metrics (e.g., delivery delays, cancellation rates), driving improvement in the e-commerce experience through predictive analytics.

- Develop multi-task models using MMoE and dynamic loss functions, driving measurable improvements in platform product, service, and logistics health.

- Transform supply chains by optimizing warehouse product quality qualification strategies to cut missing recalls and reduce warehouse costs.

- Unleash RPD/RPR growth through causal inference frameworks, identifying hidden correlations between CCR and user behavior to design personalized recommendations.

- Optimize LLM reasoning with DPO/GRPO to enhance fraud detection, outperforming traditional SFT methods. Enhance tabular data modeling for better explainability in e-commerce risk assessments.

- Construct heterogeneous graphs modeling product - seller - creator - video - user relationships, enabling improvements in product lifecycle insights and detection of similar product patterns.

- Build unified time-series forecasting models across products, sellers, creators, videos, and users, achieving SOTA performance in predicting inventory shortages and demand surges.