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Recommender systems have seen a rapid rise of application in various industries, with several services now being implemented online. Over the years, various authors have been researching on how to improve the recommender system's performance and outcome with various algorithms and evaluation criteria. This research considers users as the end consumer of the recommender system and develops a systematic literature review for its evaluation from a user-centric perspective. It first presents the evaluation categories from a system testing view and then presents a novel framework of recommender system evaluation from a user feedback perspective. The study then presents a useful timeline for researchers who intend to use mathematical evaluation metrics in recommender system research. The three-step approach to this literature review will assist future research in getting a focused user-centric view on the recommender system's evaluation.