Music Recommendation Systems: Techniques, Use Cases, and Challenges

Music recommendation systems have become increasingly popular due to the massive amount of music data available on the internet. These systems aim to provide personalized and relevant music recommendations to users based on their listening history and preferences. This paper provides an overview of the techniques used in music recommendation systems, including clustering, classification, regression, matrix factorization, neural networks, association rules, and hybrid techniques. Moreover, the paper highlights the challenges faced by music recommendation systems, including the cold start problem, data sparsity, subjectivity, diversity, and scalability. The paper concludes by discussing the future scopes of music recommendation systems, including personalization, multimodal recommendation, explainability, interactivity, integration, real-time recommendation, and ethical considerations. Overall, this paper provides a comprehensive overview of the state-of-the-art in music recommendation systems, their applications, and the challenges that need to be addressed for their wider adoption.