Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization
arXiv:2605.28810v1 Announce Type: new Abstract: Functional music applications, from consumer focus and sleep aids to clinical interventions, share a distinctive recommendation problem: success is defined by the listener's affective state, but online experimentation on emotion is ethically constrained, particularly for clinical populations who cannot reliably skip a song or report distress. We describe AMRS, the Affective Music Recommendation System deployed on LUCID's health-and-wellness platforms, which serve clinical users (primarily older adults with neurocognitive conditions) and consumer-
The increasing sophistication of AI models and the growing demand for personalized health and wellness solutions converge to enable more targeted and ethically sound applications like affective music recommendation.
This development signals a critical advancement in applying AI to sensitive domains, particularly where direct human feedback is challenging, paving the way for scalable, non-invasive therapeutic interventions.
The ability to optimize AI-driven recommendations based on 'offline' affective states, specifically for vulnerable populations, transforms how personalized therapeutic media can be developed and deployed without real-time ethical dilemmas.
- · LUCID (health-and-wellness platforms)
- · Neurocognitive care sector
- · AI-driven personalized health companies
- · Digital therapeutics developers
- · Traditional music streaming services (without affective AI)
- · Clinical research requiring extensive online experimentation
AI-powered therapeutic media for emotional regulation becomes more widespread and ethically deployable in clinical settings.
This methodology could be adapted to other sensitive applications beyond music, such as personalized content for education or chronic pain management.
The success in clinical populations could drive demand for stricter ethical guidelines and specialized AI development for vulnerable user groups across other industries.
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Read at arXiv cs.LG