Core Viewpoint - Kuaishou faced a significant black market attack on December 22, leading to a temporary shutdown of its live streaming services, which raised concerns about the platform's security measures and response time [1][2][3] Group 1: Incident Overview - The attack occurred around 10 PM on December 22, causing a widespread disruption in live streaming across the platform, with users reporting a sudden halt in content [1] - During the incident, a user witnessed inappropriate content being streamed for less than a minute, with online viewers peaking at 260,000 before the stream was abruptly closed [3][5] - Kuaishou confirmed the attack and stated that they had reported the incident to relevant authorities and were in the process of addressing the issue [1][2] Group 2: User Impact and Reactions - Many users were unaware of the attack and assumed the platform was experiencing technical difficulties, leading to confusion among content creators and viewers alike [5] - Following the incident, Kuaishou's app saw a surge in downloads, ranking second in the free app category on the Apple App Store, despite the ongoing issues [2] - There were rumors circulating on social media that the compromised streams contained virus links, leading to potential account theft and scams targeting users [5] Group 3: Security Concerns - Experts indicated that the platform's existing content moderation systems were overwhelmed by the sudden influx of inappropriate content, highlighting a need for improved security measures [1][3] - The decision to halt live streaming took approximately two hours, raising questions about the efficiency of Kuaishou's response protocols during such incidents [1][3]

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 Insights - The article emphasizes the importance of selecting the right free promotion platforms to maximize user engagement and reach target audiences effectively in the information-overloaded internet era [1][3]. Group 1: Overview of Free Promotion Platforms - The article provides a comparative analysis of five mainstream free promotion platforms, detailing their user demographics, content styles, and best-use scenarios [3]. - The platforms include: - **Commercial Cooperation**: Zhilifang, targeting promoters and business owners for project matching and resource collaboration [3]. - **Knowledge Community**: Zhihu, appealing to educated and professional users for authoritative brand building [3]. - **Content Community**: Xiaohongshu, focusing on young female consumers for product promotion through authentic sharing [3]. - **Short Video**: Douyin, catering to all age groups with creative short videos for brand exposure [3]. - **Search Information**: Baijiahao, targeting users seeking specific information for long-term content retention [3]. Group 2: Platform-Specific Strategies - **Zhihu**: It is highlighted as a premium knowledge-sharing platform where businesses can establish authority by answering relevant questions with valuable insights [4][6]. - **Zhilifang**: This platform is noted for its strict verification process for projects and users, ensuring a reliable environment for direct promotion and resource matching, with a 40% increase in order efficiency due to an upgraded matching system [6][9]. - **Xiaohongshu**: The platform's "grass-planting" culture is emphasized, where high-quality visuals and authentic content significantly impact user engagement, with conversion rates exceeding 15% for well-crafted posts [11]. - **Douyin**: The platform's strength lies in its vast user base and recommendation algorithms, with successful campaigns achieving over 2 million views in a week through engaging content [13]. - **Baijiahao**: Leveraging its connection to Baidu, it is positioned as a tool for capturing search engine traffic, with a focus on SEO-driven content that addresses user queries directly [14]. Group 3: Best Practices and Recommendations - The article stresses the need for businesses to align their platform choice with their target audience characteristics, suggesting that platforms like Xiaohongshu are ideal for female-oriented products, while Zhihu suits professional services [15][17]. - High-quality content is identified as the key to attracting users, with recommendations for regular interaction and data analysis to refine promotional strategies [15][17]. - A long-term content strategy is advised to maintain a consistent output and effectively navigate the competitive landscape of online promotion [17].

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]

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].

Core Insights - The article discusses the importance of selecting reliable platforms for promoting the Taobao "One Yuan Purchase" project, highlighting the significant income differences based on whether users connect directly with "first-hand orders" or through intermediaries [1][2]. Group 1: Reliable Platforms - The article identifies five reliable platforms for 2025 that focus on providing first-hand resources to maximize earnings [2]. - Qialifang is recognized as a leading platform in the APP promotion industry, boasting over 2 million user resources, with over 1 million in the ground promotion segment [3][4]. - Rentuibang is noted for its stable and long-term projects, focusing on various APP registration promotion tasks, ensuring reliable income [6]. - Baotu Alliance is described as an established platform offering high-yield options, with a clear interface that facilitates easy onboarding for newcomers [7]. - Shark Lingong is highlighted as a mature platform with stable services and a wealth of high-quality first-hand projects [9]. - Qialifang Mini Program is recommended for those focusing on popular APP promotions, providing comprehensive support for freelancers and part-time workers [9]. Group 2: Industry Insights - The article emphasizes the importance of understanding commission rates and settlement cycles when selecting a platform, noting that first-hand orders typically offer 20% higher commissions than second-hand orders [13]. - It advises considering project types and personal strengths, suggesting that individuals with strong offline resources should opt for ground promotion tasks, while those skilled in online promotion should choose platforms rich in online tasks [14]. - The article highlights the significance of evaluating platform entry barriers and support services, particularly for newcomers [15][16]. Group 3: Practical Tips - Newcomers are encouraged to start with simple tasks, such as promoting widely recognized APPs, to build confidence and familiarity with the promotion process [18]. - Successful promoters often combine online and offline channels to maximize income potential, leveraging online channels to gather potential users before converting them through offline promotions [19]. - Building a personal user pool is crucial for increasing income, with recommendations to create private traffic pools to enhance customer loyalty and referral rates [20]. Group 4: Common Pitfalls - The article warns against the common issue of intermediaries profiting from price differences, stressing the importance of choosing platforms that connect directly to first-hand resources [22]. - It advises caution regarding data transparency and settlement stability, recommending thorough research on platform reputations before engagement [23]. - The article cautions against engaging in fraudulent activities, emphasizing adherence to platform rules to avoid penalties and ensure sustainable income [25].

Core Viewpoint - The report from CICC maintains the earnings forecast and outperform rating for Kuaishou-W (01024), with a target price of HKD 89, indicating an upside potential of 18% based on 15/13x 25/26 year Non-IFRS P/E [1] Group 1: OneRec Recommendation System - Kuaishou launched the end-to-end generative recommendation model OneRec during the 2Q25 earnings disclosure, which enhances user engagement through deep understanding of user behavior and dynamic adaptation [2] - OneRec's architecture significantly reduces communication and storage costs, with operational costs only 10.6% of traditional recommendation processes; it currently handles 25% of requests on Kuaishou and Kuaishou Lite, leading to increased user engagement [2] - The model is expected to improve user stickiness and time spent on the platform while reducing bandwidth and user retention costs, with potential applications in marketing and e-commerce [2] Group 2: Keling Ecosystem Development - Keling achieved revenue exceeding 250 million yuan in 2Q25, showing significant quarter-on-quarter growth; the technology has surpassed competitors in performance metrics [3] - Keling upgraded its creator program to empower creators through inspiration values, cash incentives, and Kuaishou traffic support, with AI content viewership increasing by 321% compared to six months ago [3] Group 3: Creator Ecosystem and Commercialization - The creator ecosystem is thriving, with a 100% increase in submissions from creators with over 10,000 followers and an 8% growth in the number of professional streamers [4] - The e-commerce division reports over 6.6 million commercial content posts daily, attracting more than 320 million viewers and generating revenue for 3.7 million creators [4] - Kuaishou is exploring monetization opportunities in emerging content areas such as short dramas and mini-games, expecting to generate significant revenue for creators in the coming year [4]

Group 1 - The article discusses various online platforms that allow users to earn money by watching advertisements, highlighting the skepticism surrounding their legitimacy and payout thresholds [1] - Douyin's "Jisu" version is mentioned as a prominent platform, offering a dedicated earning center where users can earn up to 8200 coins by completing tasks related to advertising conversion [1] - Uke Direct Talk serves as a bridge to access resources for earning coins on platforms like Douyin and Kuaishou, while also providing opportunities to create advertising apps for monetization [3] Group 2 - Kuaishou's "Jisu" version is noted for its popularity among a wide demographic, featuring a task center where users can earn coins by watching ads, especially during major shopping events [5] - Tencent Video is identified as a newer platform for earning money through ads, with users able to earn a significant amount by completing tasks available in the coin center [5] - Tomato Novel, developed by ByteDance, allows users to earn coins by listening to novels and watching ads simultaneously, enhancing the earning potential without additional time investment [7] Group 3 - The article encourages users to explore these five platforms during their free time, suggesting that even small earnings can be beneficial [9]

Core Viewpoint - The article discusses the transformation of recommendation systems through the integration of large language models (LLMs), highlighting the introduction of the "OneRec" system by Kuaishou, which aims to enhance efficiency and effectiveness in recommendation processes [2][35]. Group 1: Challenges in Traditional Recommendation Systems - Traditional recommendation systems face significant challenges, including low computational efficiency, conflicting optimization objectives, and an inability to leverage the latest AI advancements [5]. - For instance, Kuaishou's SIM model shows a Model FLOPs Utilization (MFU) of only 4.6%/11.2%, which is significantly lower than LLMs that achieve 40%-50% [5][28]. Group 2: Introduction of OneRec - OneRec is an end-to-end generative recommendation system that utilizes an Encoder-Decoder architecture to model user behavior and enhance recommendation accuracy [6][11]. - The system has demonstrated a tenfold increase in effective computational capacity and improved MFU to 23.7%/28.8%, significantly reducing operational costs to just 10.6% of traditional methods [8][31]. Group 3: Performance Improvements - OneRec has shown substantial performance improvements in user engagement metrics, achieving a 0.54%/1.24% increase in app usage duration and a 0.05%/0.08% growth in the 7-day user lifecycle (LT7) [33]. - In local life service scenarios, OneRec has driven a 21.01% increase in GMV and an 18.58% rise in the number of purchasing users [34]. Group 4: Technical Innovations - The system employs a multi-modal fusion approach, integrating various data types such as video titles, tags, and user behavior to enhance recommendation quality [14]. - OneRec's architecture allows for significant computational optimizations, including a 92% reduction in the number of key operators, which enhances overall efficiency [27][28]. Group 5: Future Directions - Kuaishou's technical team identifies areas for further improvement, including enhancing inference capabilities, developing a more integrated multi-modal architecture, and refining the reward system to better align with user preferences [38].

Core Insights - The article discusses three promising industries for future entrepreneurship, emphasizing the potential for financial success if the right direction is chosen [1][4]. Industry Trends - The app user acquisition industry is highlighted as an undervalued sector with significant growth potential over the next five years, accessible even to individuals without specialized skills [1][3]. - The gaming peripheral market is identified as having a vast market space, particularly for independent games that have not yet released merchandise, suggesting a focus on niche products like figurines and keychains [3][4]. Business Opportunities - The article suggests that the app user acquisition industry has low entry barriers, allowing many individuals to engage in it as a side job, provided they possess certain communication skills and a strong work ethic [3][4]. - Current commission rates for app user acquisition range from 20 to 50 yuan for consumer-facing apps, while business-facing apps can yield commissions of 100 to 200 yuan per task, indicating a lucrative profit margin [3][4]. - The article encourages exploring less saturated markets, such as short drama promotion and private domain traffic, as viable avenues for generating income through online channels [4].