Comparative Analysis of Different Approaches to the Music Recommendation System

Abstract

Digital music and music streaming services have led to a significant expansion in the variety of music available. For anyone, sorting through this music would be impossible. Music recommendation systems that use genre, artist, instrument, and user evaluations to automatically suggest music greatly reduce the amount of manual effort required. Although commercial use of music recommendation systems is widespread, there is no perfect system that can offer the customer the greatest music recommendations with the least amount of human effort. This research examined various recommendation systems currently in use, including content-based, collaborative, emotion-based, and other approaches. In addition to discussing the various recommendation methods, this research also explored the advantages and disadvantages of each approach. Finally, an overview of a potential music recommendation system that addresses many of the challenges faced by current hybrid recommendation systems was provided.