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Research on user-centric systems focuses on understanding and leveraging user data and interactions to improve various applications. Current efforts concentrate on developing personalized models using federated learning, reinforcement learning, and graph neural networks to address privacy concerns and enhance model accuracy and efficiency, often incorporating user feedback and attention mechanisms. This work is significant for advancing personalized AI systems across diverse domains, from recommendation systems and conversational AI to healthcare and cybersecurity, while simultaneously mitigating privacy risks. The development of robust and explainable models that effectively capture user intent and preferences is a key challenge driving ongoing research.