刚刚，马斯克开源基于 Grok 的 X 推荐算法：Transformer 接管亿级排序

Core Viewpoint - Elon Musk's company has open-sourced the X recommendation algorithm, which supports the "For You" feed by combining in-network and out-of-network content using a Grok-based Transformer model [1][9][12]. Group 1: Algorithm Functionality - The recommendation algorithm generates content for users' main interface from two primary sources: content from accounts they follow (In-Network) and other posts discovered on the platform (Out-of-Network) [3][4]. - The algorithm filters out low-quality, duplicate, or inappropriate content to ensure that only valuable candidates are processed for ranking [4][6]. - The core of the algorithm is a Grok-based Transformer model that scores each candidate post based on user behavior such as likes, replies, and shares, predicting the probability of various interactions [4][20]. Group 2: 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 included parts of the Twitter source code [9][11]. - Musk's commitment to transparency in the algorithm is seen as a response to criticism regarding the platform's content distribution mechanisms, which have been accused of bias [12][18]. Group 3: User Reactions - Users on the X platform have summarized key points about the recommendation algorithm, noting that engagement metrics like replies significantly impact visibility, while links in posts can reduce exposure [14][15]. - Some users have observed that while the architecture is open-sourced, certain elements remain undisclosed, indicating that the release is more of a framework than a complete engine [17]. Group 4: 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: Amazon (35%), Netflix (80%), and YouTube (70%) [18]. - The complexity of traditional recommendation systems has led to a desire for a unified model that can handle multiple tasks, a goal that large language models (LLMs) may help achieve [21][22]. Group 5: Technical Insights - The open-sourced algorithm lacks specific weight parameters and internal model parameters, which limits understanding of its decision-making processes [20]. - The introduction of LLMs into recommendation systems allows for a more abstract approach to feature engineering, enabling the model to understand and process user preferences without explicit instructions [22][23].