Machine Learning-Based Music Classiffcation and Recommendation System from Spotify

Abstract

Music holds signiffcant importance in our daily lives, serving as an earnest element. Amidst the vast expanse of available information, our objective lies in sieving through data to present users with relevant music or song content aligning with their interests and objectives. To improve music recommendation accuracy and real-time recommendation ability, we propose a hybrid music recommendation model based on a Popularity-based Song Recommender System, a Personalized Song Recommender System, and a Content-based Music Recommendation System. Our endeavor focuses on constructing a music recommendation system, which operates as a ffltering mechanism predicting user preferences in music based on their inclinations. This approach employs content ffltering techniques leveraging data characteristics to reffne recommendations. Our model, tailored to accommodate diverse instances and datasets, demonstrates robust performance. The devised song recommendation system exhibits notable efffcacy and sustained reliability. Encouraging outcomes, boasting an accuracy of 90.4%, furnish novel insights, laying the groundwork for future exploration in song recommendation systems.