ConnectionMind：利用社交网络与大语言模型实现 Meta 平台的个性化推荐
ConnectionMind: Leveraging Social Networks and Large Language Models for Personalized Recommendation at Meta
浏览论文内容
中文总结 AI 辅助
该研究提出 ConnectionMind 框架，结合社交网络与 LLM，经两阶段学习训练后，在 Meta 部署并通过 A/B 测试实现视频观看时长 0.43% 的提升，解决传统推荐模型的多关系上下文整合不足问题。
中文摘要 AI 辅助
Meta 等社交媒体平台的现代推荐系统必须对复杂的社交关系（包括友谊、群组成员关系、创作者互动）以及文本、视频等海量异构内容进行建模。然而，传统推荐模型往往会忽略这些信号或对其进行独立处理，缺乏整合多关系上下文以实现细粒度个性化的推理能力。我们提出了 ConnectionMind，这是一种可投入生产使用的推荐框架，它将社交网络结构与大语言模型（LLM）紧密结合，以在 Meta 平台实现可扩展、可解释且具备推理感知能力的个性化推荐。ConnectionMind 构建了一个连接用户、物品、好友、群组和创作者页面的异构图，并将推荐任务建模为图推理问题：发现用户到候选物品的个性化路径。基于 LLM 的策略被用于对这些图结构进行推理并指导推荐决策。为了大规模训练该系统，ConnectionMind 采用了两阶段学习策略：首先对大规模用户-物品交互轨迹执行监督微调（SFT）以初始化推理策略，随后通过端到端强化学习（RL）优化模型对社交图进行推理以实现个性化推荐的能力。在多个真实世界数据集上开展的大量实验表明，与代表性基线相比，ConnectionMind 具有有效性。更重要的是，ConnectionMind 已部署到 Meta 的大规模推荐流程中，并通过在线 A/B 测试进行了评估，实现了视频观看时长提升 0.43%。这些结果证明了其在生产环境推荐系统中可衡量的实际应用价值。
英文摘要
Modern recommendation systems on social media platforms such as Meta must model complex social relationships, including friendships, group memberships, and creator interactions, alongside massive and heterogeneous content such as text and video. Traditional recommendation models, however, often omit these signals or treat them independently, lacking the reasoning capability to integrate multi-relational context for fine-grained personalization. We present ConnectionMind, a production-ready recommendation framework that tightly integrates the social network structure with large language models (LLMs) to enable scalable, interpretable, and reasoning-aware personalization in Meta. ConnectionMind constructs a heterogeneous graph connecting users, items, friends, groups, and creator pages, and formulates recommendation as a graph reasoning problem: discovering personalized paths from users to candidate items. An LLM-based policy is employed to reason over these graph structures and guide recommendation decisions. To train the system at scale, ConnectionMind adopts a two-stage learning strategy. We first perform supervised fine-tuning (SFT) on large-scale user-item interaction trajectories to initialize the reasoning policy, followed by end-to-end reinforcement learning (RL) to refine the model's ability to reason over social graphs for personalized recommendation. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of ConnectionMind compared to representative baselines. More importantly, ConnectionMind has been deployed in Meta's large-scale recommendation pipeline and has been evaluated through online A/B tests, achieving a 0.43% improvement in video watch time. These results demonstrate measurable real-world impact in a production recommendation system.
