Insomnia Audio Therapy Mobile Application with Music Recommender System

Abstract

With the quick advancement of Internet and artificial intelligence technologies, development of a robust and accurate music recommendation system has become an important issue in the field of music information retrieval. Music recommendation system has been widely used in many real-life applications, including in the health domain as an alternative of therapies. This paper presents the research design and implementation of music recommendation system that possible to be used as an insomnia audio therapy in a mobile application platform. The research focused on investigating the performances of three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be selected as the music recommendation tool. For the machine learning training and testing purposes, data was collected based on the simulated run of the proposed insomnia audio therapy mobile application. The results indicated that Random Forest performed as the best machine learning algorithm in predicting the relevant music. The proposed mobile application with machine learning music recommender system will provide a basis for the realization of intelligent music therapy in treating insomnia disorder patients as well as in other music applications.