A Literature Review on an Emotion Based Music Recommendation System

Abstract— Emotion-based music recommendation systems improve user experience by suggesting songs that reflect the listener’s mood. Techniques such as deep learning, facial emotion recognition, and wearable sensors to capture emotions from facial expressions, heart rate, or other signals are used. Convolutional Neural Networks (CNNs) isolate features from images for accurate emotion classification, while probabilistic models like Bayesian networks interpret uncertain emotions. By combining real-time emotion detection with recommendation algorithms, the system personalizes playlists instantly based on the user’s emotional state. This ensures a seamless and emotionally engaging listening experience for users.