References
- Tsai, C.F. and Lu, Y.H., 2009. Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), pp.12547-12553. https://doi.org/10.1016/j.eswa.2009.05.032
- Chen, C.M., Tsai, M.F., Lin, Y.C. and Yang, Y.H., 2016, September. Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 79-82). ACM.
- Dean, J. and Ghemawat, S., 2008. MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), pp.107-113. https://doi.org/10.1145/1327452.1327492
- Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S. and Stoica, I., 2010. Spark: Cluster computing with working sets. HotCloud, 10(10-10), p.95.
- Chen, M. (2019, October). Music streaming service prediction with MapReduce-based artificial neural network. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0924-0928). IEEE.
- Arora, R., Basu, A., Mianjy, P. and Mukherjee, A., 2016. Understanding deep neural networks with rectified linear units.arXiv preprint arXiv:1611.01491.
- Van den Poel, D. and B. Lariviere (2004): "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, 157, 196 - 217, smooth and Nonsmooth Optimization. https://doi.org/10.1016/S0377-2217(03)00069-9
- Wei, C.-P. and I.-T. Chiu (2002): "Turning telecommunications call details to churn prediction: a data mining approach," Expert Systems with Applications, 23, 103 - 112. https://doi.org/10.1016/S0957-4174(02)00030-1
- Chen, Z.-Y., Z.-P. Fan, and M. Sun (2012): "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, 223, 461 - 472. https://doi.org/10.1016/j.ejor.2012.06.040
- Martins, H. (2017): "Predicting user churn on streaming services using recurrent neural networks."
- Tan, F., Z. Wei, J. He, X. Wu, B. Peng, H. Liu, and Z. Yan (2018): "A Blended Deep Learning Approach for Predicting User Intended Actions," 2018 IEEE International Conference on Data Mining (ICDM), 487-496.
- Zhou, J., J.-f. Yan, L. Yang, M. Wang, and P. Xia (2019): "Customer Churn Prediction Model Based on LSTM and CNN in Music Streaming," DEStech Transactions on Engineering and Technology Research.
- Zurada, J.M., 1992. Introduction to artificial neural systems (Vol. 8). St. Paul: West publishing company.
- Verbeke, W., K. Dejaeger, D. Martens, J. Hur, and B. Baesens (2012): "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, 218, 211 - 229. https://doi.org/10.1016/j.ejor.2011.09.031
- Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- WSDM - KKBox's Music Recommendation Challenge https://www.kaggle.com/c/kkbox-music-recommendation-challenge.
- Hadden, J., A. Tiwari, R. Roy, and D. Ruta (2007): "Computer assisted customer churn management: State-of-the-art and future trends," Computers and Operations Research, 34, 2902 - 2917. https://doi.org/10.1016/j.cor.2005.11.007