Recommender systems predict user preferences to boost engagement, with applications in e-commerce and social media. Human-centered recommender systems enhance user experience via conversational interfaces and explainability, leveraging machine learning and natural language processing. From a human-centered AI (HCAI) perspective, recommender systems should be designed to prioritize human values, agency, and well-being alongside technical performance. User modeling is key for personalization, though challenges like privacy and context-awareness persist. The success of recommender systems depends on interaction quality, emphasizing transparency and user control. Future research should prioritize privacy-preserving techniques, context-aware recommendations, and diverse strategies to improve trust and satisfaction. Trust in recommender systems stems from transparent interactions and rational suggestions, fostering user reliance. Human-centered recommender systems raise ethical concerns, including manipulation risks and social polarization. Participatory design approaches are vital to align systems with user needs, ensuring ethical principles like fairness and inclusivity enhance human well-being.