EMER can rank short video content more intelligently. It's an end-to-end multi-objective ensemble ranking framework that replaces manual heuristics with learned personalization. It designs a new loss for ambiguous supervision, uses a transformer-based architecture to model candidate relationships, and ensures offline-online consistency in evaluation. Deployed at Kuaishou (hundreds of millions of users), EMER boosted app stay time by 1.39% and 7-day user lifetime by 0.196%—a major leap for industrial-scale recommendation systems. An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation Paper: Our report: