Tuned4You: A Machine Learning-Based Music Scoring Tool Using Health Data

Abstract

Music recommendation is an important aspect for music streaming service users. Studies have proven that music has effects on human emotions, cognition and health. Vice versa, numerous studies have shown that health factors such as emotional state and physical fatigue affect the music preferences of users. In spite of that, most music recommendation systems offered by large companies focus on music trends and listening history, often neglecting the influence of current emotional or health conditions of the individual. Health data from wearable devices can provide valuable insights into music preferences. This study presents a web application that integrates Spotify music data with Fitbit health metrics to train a machine learning-based scoring model. The system predicts music success scores by analyzing user behavior and health data, adapting scoring model accordingly. A supervised user study assesses the system's usability and effectiveness in adapting to user preferences through realworld interactions. Overall, this study highlights the potential of integrating health data into music recommendation systems to enhance personalization and user experience.