An Examination of Deep Learning Mechanisms for Music Recommendation Systems

Abstract: Emotions play a vital role in human experiences, and music is a powerful medium for expressing and communicating emotions. The traditional music recommendation system learns from user listening behavior but has drawbacks. Emotional recognition is essential for advancing music recommendation systems. Musical genre research is becoming increasingly important and prominent as multimedia technological developments have occurred in the past few decades. It is vital to investigate the link between music lyrics and human feelings. Music-based assessment of emotions can aid in the enhancement of a person's state of mind. This may be used in Music Recommendation Systems (MRS), advertising, and so forth. The study emphasizes the significance of facial expressions in emotion detection and communication. It examines MRS as a Machine Learning (ML)--based recommendation system for music and movies. Many academics believe that MRS software integration with platforms like Spotify and Netflix would be a significant addition to their offerings. The research advances the field of emotion-based recommendation systems and illustrates the power of ML and Deep learning (DL) in improving user experiences