When you can't safely experiment on users (especially vulnerable populations), build a world model from past data to simulate outcomes, then optimize your recommendation policy offline—this avoids ethical risks while still improving emotional outcomes.
This paper describes AMRS, a music recommendation system for clinical and wellness users that predicts how songs affect emotional states (valence and arousal) using a world model trained on listening data.