Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems

Researchers developed HAP (Heterogeneity-Aware Adaptive Pre-ranking), a new framework for recommender systems that addresses gradient conflicts in training by separating easy and hard samples. The system has been deployed in Toutiao's production environment for 9 months, achieving 0.4% improvement in user engagement without additional computational costs.