用端到端框架提升短视频推荐个性化，效果显著。
An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
- 设计新损失函数与网络结构，自动学习候选内容排序关系。
- 在快手实测中，用户停留时长增1.39%，7天留存率升0.196%。
- 适合需要多目标优化的工业级推荐系统研发人员。
我们提出一种新型端到端多目标集成排序框架（EMER），作为短视频推荐系统中最关键的组件。EMER通过端到端建模替代人工设计的启发式公式，提升个性化能力。针对集成排序缺乏有效监督信号的难题，设计了精细的损失函数；引入新的样本组织方法和基于Transformer的网络架构，以捕捉候选内容间的相对关系，这对有效排序至关重要。此外，构建了离线-在线一致的评估体系，提升离线模型优化效率，解决工业界多目标排序长期存在的挑战。在真实工业数据集上进行了充分实验，结果验证了该框架的有效性。该框架已在快手主场景部署，服务数亿日活用户，实现整体App停留时长提升1.39%，7天用户生命周期（LT7）提升0.196%，成效显著。
原文摘要 · Abstract (English)
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.
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