Core Viewpoint - The article discusses the introduction of a new ranking framework called EMER by Kuaishou, which utilizes an end-to-end multi-objective ensemble ranking approach to enhance video recommendations, addressing the limitations of traditional manual ranking methods [1][46]. Group 1: Introduction of EMER - Traditional video recommendation systems relied on manually designed formulas to rank videos based on user engagement metrics, which faced challenges in meeting diverse user preferences [1][5]. - EMER replaces this outdated method with an AI model that learns to compare videos rather than assigning independent scores, allowing for a more nuanced understanding of user preferences [5][6]. Group 2: Technical Innovations - EMER innovates at three levels: data, features, and model architecture. It uses a full candidate set for training, incorporates relative ranking information, and employs a Transformer-based model to capture relationships between videos [6][9]. - The model's ability to see all candidate videos in a single request helps mitigate exposure bias and enhances the comparison basis for ranking [7][8]. Group 3: User Satisfaction Measurement - EMER defines user satisfaction through relative satisfaction metrics rather than absolute scores, allowing the model to learn user preferences more effectively [12][14]. - It employs multi-dimensional satisfaction proxy indicators to address the sparsity of user feedback, ensuring a comprehensive understanding of user satisfaction [15]. Group 4: Self-Evolution Mechanism - EMER includes a self-evolution module that dynamically adjusts the weight of different objectives based on real-time performance, enhancing the model's adaptability to changing user behaviors [20][21]. - This mechanism has shown significant improvements in multiple metrics without the trade-offs typically seen in traditional models [21][22]. Group 5: Validation and Results - EMER has been implemented in Kuaishou's main app and has demonstrated substantial improvements in key performance indicators such as seven-day retention and app stay time, outperforming previous manual ranking methods [30][34]. - The model's effectiveness has been validated through A/B testing, showing consistent enhancements across various metrics [31][36]. Group 6: Industry Implications - EMER addresses three core challenges in the industry: defining user satisfaction, understanding the comparative nature of ranking, and establishing effective learning objectives for models [47][48]. - The framework serves as a practical reference for other companies looking to optimize their recommendation systems, showcasing its potential for broader application in the industry [49].