What to play next? A RNN-based music recommendation system

Abstract

In the very recent years, development of music recommendation system has been a more heated problem due to a higher level of digital songs consumption and the advancement of machine learning techniques. Some traditional approaches such as collaborator filtering, has been widely used in recommendation systems, have helped music recommendation system to give music listeners a quick access to the music. However, collaborative filtering or model based algorithm have limitations in giving a better result with the ignorance of combination factor of lyrics and genre. In our paper, we will propose an improved algorithm based on deep neural network on measure similarity between different songs. The proposed method will make it possible that it could make recommendations in a large system to make comparisons by understand the content of songs. In this paper, we propose an end-end model, which is based on recurrent neural network to predict user's next most possible song by similarity. We will make experiments and evaluations based on Million Song Dataset and demonstrate how it outperformed the traditional methods.