用音乐偏好提升艺术治疗推荐，让情绪感知更全面。
Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
- 通过音乐诱发情绪反应，构建跨域推荐模型
- 200人实验显示音乐引导推荐优于纯视觉方式
- 适合心理治疗、情感计算与个性化推荐研究者
艺术治疗（AT）通过创造性表达促进情绪处理与康复。近年来，视觉艺术推荐系统（VA RecSys）应运而生，通过个性化推荐辅助治疗，但现有方法仅依赖视觉刺激进行用户建模，难以捕捉完整的感情反应。已有研究表明，音乐能激发独特的情感反馈，为跨域推荐（CDR）在艺术治疗中的应用提供了契机。由于该方向尚未被探索，我们提出一套基于音乐驱动偏好获取的跨域推荐方法。一项包含200名用户的大型研究验证了其有效性，结果表明音乐驱动的偏好获取显著优于传统的纯视觉方式。项目代码、数据与模型已公开于 https://github.com/ArtAICare/Affect-aware-CDR。
原文摘要 · Abstract (English)
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
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