Natural language (NL) user interest profiles—concise, human-readable descriptions of a user’s preferences—are an emerging alternative to opaque embeddings in recommender systems, offering transparency and user control alongside predictive utility. Recent advances in large language models have made generating and consuming such profiles increasingly practical, spurring a growing but fragmented body of research. In this survey, we provide the first systematic review of NL user profiles for recommendation, organizing the literature around two coupled tasks—profile generation and profile-based recommendation—and surveying the methods, datasets, and evaluation practices currently in use. We further propose the Reference-Anchored Evaluation Taxonomy (RAET), which organizes intrinsic and extrinsic metrics around explicit (output, reference signal) pairs, and reveals where efforts have concentrated and which axes remain unexplored. Finally, we identify structural open challenges—such as the tension between transparency and task-driven optimization—establishing a clear roadmap for future research in this space.