Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.