AI/ML based-What to play next? An RNN-based music recommendation system

The rapid development of music recommendation systems has become a significant problem in the modern day, mostly as a result of the increased use of machine learning techniques and the consumption of higher quality digital songs. Collaborator filtering and other classic methods are employed more frequently in music recommendation algorithms. It aided the system in providing listeners with a full range of music. The collaborative filter is recognized to have some restrictions on producing better results and ignores elements like genre and lyrics.

The algorithm used in this paper to calculate the degree of similarity between different songs is a much improved deep neural network algorithm. The suggested approach can fully enable the possibility of producing specific recommendations in a wide system for making a thorough comparison by comprehending the substance of songs. Here, we intend to employ a recurrent neural network-based end-model to predict possible music for customers. In order to demonstrate how the Million Song Dataset surpasses many standard methodologies, we will conduct thorough evaluations and experiments on its premise.