This is a dissertation plan built around human-centered empirical experiments evaluating recommender systems (RecSys). We see this as an important research theme since many AI-based RecSys algorithmic studies lack real human assessment. Therefore, we do not know how they work in the wild that only human experiments can tell us. We split this extended abstract into two parts — 1) A series of individual studies focusing on open questions about different human values or recommendation algorithms. Our completed works include user control over content diversity, user appreciation on DL-RecSys algorithms, and human-LLMRec interaction study. We also propose three future works to understand news recommendation depolarization, personalized news podcast, and interactive user representation; 2) An experimentation infrastructure named POPROX. As a personalized news recommendation platform, it aims to support the longitudinal study needs from the general AI and RecSys research community.