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http://dx.doi.org/10.22937/IJCSNS.2022.22.1.9

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

Chen, Min (Department of Computer Science, State University of New York at New Paltz)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.1, 2022 , pp. 55-60 More about this Journal
Abstract
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.
Keywords
Music Streaming; Churn Prediction; MapReduce; Artificial Neural Network;
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