# An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation

arXiv: https://arxiv.org/abs/2508.05093 · HTML: https://mlsanity.com/paper/2508.05093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [OneReason Technical Report](https://mlsanity.com/paper/2606.06260/) (2026)
- [SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking](https://mlsanity.com/paper/2607.20655/) (2026)

## Abstract (arXiv)

We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-truth signal can fully capture user satisfaction. Moreover, EMER introduces novel sample organization method and transformer-based network architecture to capture the comparative relationships among candidates, which are critical for effective ranking. Additionally, we have proposed an offline-online consistent evaluation system to enhance the efficiency of offline model optimization, which is an established yet persistent challenge within the multi-objective ranking domain in industry. Abundant empirical tests are conducted on a real industrial dataset, and the results well demonstrate the effectiveness of our proposed framework. In addition, our framework has been deployed in the primary scenarios of Kuaishou, a short video recommendation platform with hundreds of millions of daily active users, achieving a 1.39% increase in overall App Stay Time and a 0.196% increase in 7-day user Lifetime(LT7), which are substantial improvements.