算法与算法之外：抖音内容推荐系统如何运行？

Core Viewpoint - The article discusses the inherent contradictions faced by content platforms like Douyin regarding the transparency of recommendation algorithms and their impact on user behavior and content quality [2][3]. Group 1: Algorithm Transparency and User Interaction - Douyin has established an algorithm transparency project team to explain the principles of its recommendation algorithms and its approach to content governance [4]. - The recommendation algorithm learns user behavior patterns by estimating the probability of user interactions with videos, such as likes and comments, based on historical data [5][9]. - The algorithm's effectiveness is enhanced by continuously feeding historical data into machine learning models, allowing for more accurate predictions of user behavior [9][10]. Group 2: Value Scoring and A/B Testing - Douyin determines the value score of user behaviors through A/B testing, adjusting the recommendation algorithms for different user groups to observe changes in key performance indicators [14]. - Multiple performance indicators may change in different directions, and Douyin's data analysis team creates relationships between these indicators to ensure they align with the primary goal of long-term user retention [16]. Group 3: Content Quality and Governance - Douyin's content operations team focuses on increasing the proportion of high-quality content by defining what constitutes "high-quality supply" and regularly reviewing and refining these standards [17][18]. - The platform employs a multi-layered review process for content, combining machine and human evaluations to filter out harmful or low-quality videos [19][24]. Group 4: User Behavior and Platform Adaptation - Douyin's governance measures are influenced by user behavior, with the platform adapting to user habits and preferences, such as the use of alternative terms to avoid perceived censorship [24][25]. - The platform has established a dedicated team to identify and counteract rumors, utilizing a "rumor database" to train machine learning models for better detection of false information [25][26]. Group 5: Evolving Standards and Regulatory Response - The article highlights the ongoing evolution of content platforms in response to public scrutiny and regulatory frameworks, emphasizing the need for continuous adaptation to societal expectations [28][29][30].