刚刚，马斯克开源基于 Grok 的 X 推荐算法！专家：ROI 过低，其它平台不一定跟

Core Viewpoint - Elon Musk has open-sourced the X recommendation algorithm, which combines in-network content from followed accounts and out-of-network content discovered through machine learning, using a Grok-based Transformer model for ranking [3][12][18]. Summary by Sections Algorithm Overview - The open-sourced algorithm supports the "For You" feed on X, integrating content from both followed accounts and broader network sources, ranked by a Grok-based Transformer model [3][5]. - The algorithm fetches candidate posts from two main sources: in-network content (from accounts users follow) and out-of-network content (discovered through machine learning) [9][10]. Algorithm Functionality - The system filters out low-quality, duplicate, or inappropriate content to ensure only valuable candidates are processed [7]. - A Grok-based Transformer model scores each candidate post based on user interactions (likes, replies, shares, clicks), predicting the probability of various user actions [7][8]. Historical Context - This is not the first time Musk has open-sourced the X recommendation algorithm; a previous release occurred on March 31, 2023, which garnered over 10,000 stars on GitHub [12][14]. - Musk aims to enhance transparency in the algorithm to address criticisms regarding bias in content distribution on the platform [18][19]. User Reactions - Users on the X platform have summarized key insights about the recommendation algorithm, emphasizing the importance of engagement metrics like replies and watch time for content visibility [22][23]. Importance of Recommendation Systems - Recommendation systems are crucial to the business models of major tech companies, with significant percentages of user engagement driven by these algorithms (e.g., 35% for Amazon, 80% for Netflix) [25][27]. - The complexity of traditional recommendation systems often leads to high maintenance costs and challenges in cross-task collaboration [28]. Future Implications - The introduction of large language models (LLMs) presents new opportunities for recommendation systems, potentially simplifying engineering and enhancing cross-task learning [29][30]. - The open-sourcing of the X algorithm may not lead to immediate changes across other platforms, as they may lack the resources to implement similar systems [39].