快手-W是香港联交所上市公司（股票代码：1024.HK），作为中国领先的内容社区和社交平台，旗下拥 有快手主App和快手极速版两大产品。截至2023年三季度，快手应用平均日活跃用户达3.87亿，电商交 易总额同比增长30.4%至2902亿元。公司主营业务包含线上营销服务、直播打赏及电商业务三大板块， 2023年前三季度总收入达904亿元。值得注意的是，快手在2021年2月以"短视频第一股"身份登陆港股， 发行价115港元，目前股价处于历史低位区间。公司持续加码AI技术研发，2023年研发投入超百亿元， 其独特的"老铁经济"生态和下沉市场优势仍是核心竞争壁垒。 本文源自：市场资讯 12月18日，快手-W回购123.50万股股票，每股回购均价64.95港元，共耗资约8021.93万港元，本年累计 回购5131.10万股，占总股本1.18%。 截至当日港股收盘，快手-W上涨0.23%，报65.35港元/股。 快手-W近期回购情况 回购日期回购均价回购股数回购金额本年累计回购股数2025-12-1864.955123.50万8021.93万5131.10万 2025-12-1764.721128.30万8303.70 ...

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 - EMER replaces this outdated method with an AI model that learns to compare videos rather than assigning independent scores, allowing for a more nuanced understanding of user preferences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].