Rnn-Based Music Recommendation System

This project aims to create a music recommendation system based on RNN (Recurrent Neural Network) and face emotion recognition. The system will use the face emotion recognition algorithm to detect the user’s emotion and then use the RNN to generate music recommendations accordingly. The system will use a dataset of music and facial expressions to train the RNN. The trained RNN will then be used to generate music recommendations for the user. The system will also be able to adjust the music recommendations based on the user’s changing facial expressions. It is capable of learning from user feedback and continuously improving its recommendations. The system can also detect patterns in user behavior and make predictions on what type of music they might enjoy. This project can benefit music streaming services by providing a personalized music experience for each user. Finally, it uses a variety of metrics to evaluate the accuracy of its recommendations.

Applications

• Have various applications in the music industry, such as in streaming music services, radio stations, and online music stores.
• It can be used to identify trends in music consumption and recommend songs that are likely to be popular.
• This can help music producers and promoters to better understand their target audience and optimize the marketing of their music.