Robots are increasingly moving from industrial applications into everyday human environments such as healthcare, households, and public spaces. In these interactive and personal contexts, successful human-robot interaction (HRI) critically depends on robots' abilities to interpret, reflect, and adapt to individual human preferences. Yet traditional robot navigation methods, though reliable in structured environments, generally fail to capture and reflect nuanced user preferences, resulting in suboptimal user experience, reduced trust, and limited acceptance.To address these shortcomings, this thesis presents a comprehensive approach toward personalized, learning-based robot navigation. It specifically focuses on four critical aspects: (1) efficient and intuitive collection of human preferences, (2) balancing user preference reflection with robot navigation goals, (3) deriving expressive sensor representations suitable for dynamic environments, (3) deriving expressive sensor representations suitable for dynamic environments, and (4) ensuring adaptability and transparency in HRI once deployed on a robot.