Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
This work addresses the problem of enhancing personalization in Art Therapy for users by leveraging music stimuli, representing an incremental improvement over existing methods.
The paper tackled the limitation of visual-only user modeling in Visual Art Recommender Systems for Art Therapy by introducing cross-domain recommendation methods based on music-driven preference elicitation, and a large-scale study with 200 users showed it outperformed the classic visual-only approach.
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