Abstract

Music is widely regarded as nourishment for the human mind and a universal cultural expression that supports emotional well-being. Its capacity to modulate emotional states, promote adaptive behaviors, and mitigate mental health challenges underscores its significance as a component of therapeutic interventions. However, selecting appropriate music genres, identifying optimal listening durations, and designing effective AI-driven personalization mechanisms remain significant challenges. This paper proposes a framework and presents the development of a personalized music recommendation application, guided by the Design Science Research (DSR) framework, to support intelligent music-based therapeutic interventions. Recommender systems assist users in navigating vast music libraries by extracting meaningful patterns from large-scale datasets and aligning recommendations with individual preferences. Following the DSR process of problem identification, artifact design, development, and evaluation, the proposed framework integrates collaborative filtering with content-based techniques using Cosine Similarity and Count Vectorization. Python APIs and datasets sourced from a popular music platform are utilized to implement and validate the model. Given music’s historical and cross-cultural role in emotional regulation, an intelligent recommendation approach can significantly enhance therapeutic outcomes. Managing extensive user interaction logs and diverse music metadata introduces computational complexity, requiring efficient algorithmic design. Experimental results demonstrate effective genre prediction and improved personalized recommendation performance using standard evaluation metrics. This research emphasizes the importance of user mood, behavioral trends, and activity patterns in advancing AI-enhanced music therapy systems.

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