Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation

TL;DR

This paper introduces a novel cross-domain recommendation approach for art therapy that leverages music preference elicitation to improve personalization, demonstrating significant benefits over visual-only methods in a large-scale user study.

Contribution

It pioneers the application of music-driven cross-domain recommendation in art therapy, enhancing emotional response capture beyond visual stimuli.

Findings

Music-driven preference elicitation outperforms visual-only methods.

Large-scale study with 200 users validates the approach.

Source code and data are publicly available.

Abstract

Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression. Recently, Visual Art Recommender Systems (VA RecSys) have emerged to support AT, demonstrating their potential by personalizing therapeutic artwork recommendations. Nonetheless, current VA RecSys rely on visual stimuli for user modeling, limiting their ability to capture the full spectrum of emotional responses during preference elicitation. Previous studies have shown that music stimuli elicit unique affective reflections, presenting an opportunity for cross-domain recommendation (CDR) to enhance personalization in AT. Since CDR has not yet been explored in this context, we propose a family of CDR methods for AT based on music-driven preference elicitation. A large-scale study with 200 users demonstrates the efficacy of music-driven preference elicitation, outperforming the classic visual-only elicitation approach. Our source code, data, and models are available at https://github.com/ArtAICare/Affect-aware-CDR