Connecting Autonomous Recommender Systems through Federated Semantic Grounding

Autonomous recommender systems learn from different item offerings and behavioral feedback, and thus acquire complementary knowledge about items and user preferences. However, such knowledge is deeply embedded in system-specific recommendation spaces, making direct cross-system transfer difficult. Federated recommendation enables decentralized knowledge exchange, but existing methods often rely on entity correspondence or compatible model and representation spaces. LLMs offer a shared semantic basis across systems, but their generic semantics do not capture the recommendation meaning learned from local interactions. We propose Federated Semantic Grounding, which grounds shared semantics in local behavior and uses them to transfer recommendation knowledge across systems. The framework applies this principle at both the item and user levels. At the item level, we use shared semantic concepts as anchors to exchange complementary item semantics across different item spaces. At the user level, we re-express locally learned preferences in another system's item context and coordinate their interpretation using mappings learned from local behavior. Experiments across diverse collaboration scenarios show consistent improvements over independent and federated baselines, and further demonstrate that FedSG remains effective when participating systems differ in model architecture or local recommendation setting. These results support semantic grounding as an effective basis for collaboration among autonomous recommender systems. The code is available at: https://anonymous.4open.science/r/FedSG-683D/.