Music Recommendation System Using Machine Learning

Music recommendation systems function as personalized assistants that analyze listener preferences and suggest relevant songs or playlists. These systems utilize past user data to generate recommendations that align with individual tastes. However, users often struggle to identify the most suitable songs due to the vast availability of music content. Various techniques have been employed to enhance recommendation accuracy, including collaborative filtering, content-based filtering, and hybrid models. Initially, the system gathers substantial user data, such as listening history and ratings, to create comprehensive listener profiles. Several machine learning algorithms, such as Cosine Similarity, K-Nearest Neighbors (KNN), and the Weighted Product Method, can be implemented for effective recommendations. Advanced hybrid approaches, integrating Singular Value Decomposition and Factorization Machines, further optimize recommendation accuracy. These systems are a specialized application of machine learning, leveraging diverse techniques to analyze user behavior and deliver personalized music recommendations. This paper presents a Music Recommendation System that incorporates multiple advanced technologies to improve accuracy and user experience.