Core Insights - Kuaishou has launched a new end-to-end multi-objective fusion ranking framework called EMER, which enhances user retention and engagement metrics significantly [1][3][6] Group 1: Traditional Recommendation Challenges - The traditional recommendation system relied on manually designed formulas, which struggled to meet the complex and personalized needs of millions of users [2] - The limitations of the traditional approach included difficulties in balancing conflicting goals such as user retention and video views, leading to challenges in precise parameter tuning [2] Group 2: EMER Framework Innovations - EMER's core breakthrough is its ability to enable AI models to compare and select from a batch of candidate videos, aligning more closely with real-world recommendation scenarios [2] - The framework employs a method system based on "relative advantage satisfaction + multi-dimensional satisfaction proxy indicators," allowing for effective supervision and continuous optimization of user satisfaction [2] Group 3: Performance Metrics - The EMER framework has demonstrated significant improvements in key performance metrics: - Kuaishou's app saw a 0.133% increase in seven-day retention and a 1.199% increase in user stay time - The Kuaishou Lite version experienced a 0.196% increase in retention and a 1.392% increase in stay time - Video views increased by 2.996% [3][4] Group 4: Cross-Scenario Application - EMER has been successfully integrated into Kuaishou's end-to-end generative recommendation system, OneRec, resulting in an additional 0.56% increase in stay time, showcasing its robust cross-scenario and cross-link reuse capabilities [6]