Core Viewpoint - The article discusses the achievements and ongoing efforts in algorithm governance in Beijing, highlighting the collaboration between government agencies, internet companies, and experts to address algorithm-related issues and improve user experience. Group 1: Algorithm Governance Initiatives - Beijing's internet authority and related departments have launched a special action to address 27 specific algorithm-related issues since November of last year [1] - Major platforms like Douyin, Kuaishou, and Meituan have implemented various measures to enhance algorithm governance, such as Douyin's verification mechanism for hot topics and Kuaishou's self-assessment features [1][2] - Meituan has organized over 100 rider forums and introduced measures like penalty waivers for delays and mandatory rest periods to balance management and social responsibility [1] Group 2: Expert Insights and Recommendations - Experts emphasize the need for algorithm governance to balance efficiency and fairness, suggesting the establishment of four common paths: algorithm transparency, data protection, impact assessment, and anti-discrimination governance [2] - The challenges posed by the gig economy, such as blurred labor standards and hidden algorithm biases, require a legal framework that integrates corporate self-regulation into institutional practices [2] Group 3: Future Directions and Goals - The Beijing internet authority aims to strengthen compliance awareness among local platforms and enhance technical innovation to tackle governance challenges [3] - There is a focus on improving algorithm safety by addressing weak points in recommendation systems, such as content value orientation and user interest exploration [3] - The authority plans to further research and implement effective measures for algorithm governance, transitioning towards a more normalized governance approach [3]

Core Insights - Kuaishou has launched new algorithm recommendation optimization features aimed at enhancing user experience and preventing "information cocoons" [2] Group 1: New Features - The platform has introduced two personalized recommendation optimization features: "Information Cocoon Self-Assessment" and "One-Click Breakthrough" [2] - The "Information Cocoon Self-Assessment" allows users to visualize the distribution of content types, preferences, and recommendation intensity on their recommendation page [2] - The "One-Click Breakthrough" feature enables users to adjust the types of recommended content, interest tags, and push intensity based on their preferences [2] Group 2: User Control - Users can access these features by navigating to the "Content Preference" module in the app, where they can view their interest tag distribution and content type [2] - After assessment, users can choose to maintain their current personalized recommendations or add more content type tags to their recommendation page [2] - Users have the option to turn off personalized recommendations entirely if they find the content uninteresting by adjusting settings in the app [3]