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ConnectionMind is a novel recommendation framework that integrates social network structures with large language models (LLMs) to enhance personalization in Meta's recommendation systems. By modeling complex relationships such as friendships and group memberships within a heterogeneous graph, the framework formulates recommendation as a graph reasoning problem, allowing for more nuanced and context-aware suggestions. The approach has been validated through extensive experiments and real-world deployment, resulting in a 0.43% increase in video watch time, showcasing its practical effectiveness in a production environment.
Personalized recommendations can now leverage social network dynamics, leading to a measurable 0.43% increase in user engagement on Meta's platform.
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.
Meta AI