Personalized federated recommendation via long-horizon local optimization and regularized knowledge guidance
A model-agnostic framework that forms personalized item embeddings through Long-Horizon Local Optimization and injects common global knowledge through intermittent Regularized Knowledge Guidance is proposed and Adaptive Guidance is introduced to control the influence of global knowledge at the user–item interaction level.