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A MapReduce-based Artificial Neural Network Churn Prediction for Music Streaming Service


Min Chen


Vol. 22  No. 1  pp. 55-60


Churn prediction is a critical long-term problem for many business like music, games, magazines etc. The churn probability can be used to study many aspects of a business including proactive customer marketing, sales prediction, and churn-sensitive pricing models. 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.


Music Streaming, Churn Prediction, MapReduce, Artificial Neural Network