Music recommendation systems are algorithms designed to suggest songs, artists, or playlists to users based on their listening habits and preferences. These systems analyze data such as user interactions, song characteristics, and trends to provide personalized recommendations, enhancing the overall music discovery experience. These systems often utilize techniques like collaborative filtering, which considers the preferences of similar users, and content-based filtering, which focuses on the attributes of the music itself. Popular platforms like Spotify and Apple Music employ these systems to help users find new music that aligns with their tastes.