Music Streaming Service Prediction with MapReduce-based Artificial Neural Network

Abstract

The problem on accurately predicting customer churn is critical to the long-term success in subscription business like music, games, magazines etc. It is quite challenging to design machine learning model to predict the customer churn accurately due to the large volume of the time-series data and the temporal issues of the data. In this paper, a parallel artificial neural network is proposed to create a highly-accurate customer churn model on a large customer dataset. The proposed model has achieved significant improvement in the accuracy of churn prediction. The scalability and effectiveness of the proposed algorithm is also studied.