A Novel Approach for Song Recommendation System Using Deep Neural Networks

The song recommendation model is a powerful tool for improving user experience and engagement with streaming music services. Depending on user emotion recognition, the proposed Song suggester Convolutional Neural Network (CNN). The proposed model uses facial expressions to recognize the user's emotions, which are then used to recommend songs that match their mood. The model is trained on a large dataset of facial expressions and songs with emotional labels. The facial expression images are pre-processed using various image augmentation techniques and then fed into the CNN model for feature extraction. The extracted features are then used to predict the user's emotions, which are mapped to the corresponding emotional labels of songs. The recommended songs are then presented to the user, who can choose to add them to their playlist. The proposed model outperforms existing emotion-based song recommendation models and has the potential to enhance user experience in music streaming services with an accuracy of 93%.