REMEDI: Relative Feature Enhanced Meta-Learning with Distillation for Imbalanced Prediction
This paper addresses the dual challenges of extreme class imbalance (positive sample rate < 0.5%) and user behavior heterogeneity in vehicle purchase prediction. To tackle these, we propose a novel multi-stage modeling framework: (1) constructing diverse base models to capture behavioral heterogeneity; (2) designing innovative relative-performance meta-features—such as prediction bias and peer-ranking scores—to guide meta-learning-based ensemble fusion; and (3) applying business-objective-oriented supervised knowledge distillation to compress the ensemble into a lightweight, deployable single model. The approach synergistically integrates meta-learning, ensemble learning, knowledge distillation, and relative feature engineering. Evaluated on a real-world dataset of 800,000 car owners, our method achieves 10% precision in top-60,000 recommendations, covering 50% of actual buyers—substantially outperforming state-of-the-art baselines. The distilled model retains over 98% of the ensemble’s predictive performance while significantly improving inference efficiency and operational scalability.