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From the earliest days of the field, Recommender Systems research and practice has struggled to balance and integrate approaches that focus on recommendation as a machine learning or missing‐value problem with ones that focus on machine learning as a discovery tool and perhaps persuasion platform. In this article, we review 25 years of recommender systems research from a human‐centered perspective, looking at the interface and algorithm studies that advanced our understanding of how system designs can be tailored to users objectives and needs. At the same time, we show how external factors, including commercialization and technology developments, have shaped research on human‐centered recommender systems. We show how several unifying frameworks have helped developers and researchers alike incorporate thinking about user experience and human decision‐making into their designs. We then review the challenges, and the opportunities, in today's recommenders, looking at how deep learning and optimization techniques can integrate with both interface designs and human performance statistics to improve recommender effectiveness and usefulness