Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (5): 167-178.doi: 10.3969/j.issn.1000-5641.202091016
• Application of Data Platform • Previous Articles Next Articles
XU Yiwen, LI Xiaoyang, DONG Qiwen, QIAN Weining, ZHOU Fang
|  BHATTACHARYA C B. When customers are members: Customer retention in paid membership contexts [J]. Journal of the Academy of Marketing Science, 1998, 26(1): 31-44.
 REICHHELD F, DETRICK C. Loyalty: A prescription for cutting costs [J]. Marketing Management, 2003, 12(5): 24-24.
 HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
 BAYTAS I M, XIAO C, ZHANG X, et al. Patient subtyping via time-aware LSTM networks [C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 65-74.
 FENG W, TANG J, LIU T X. Understanding dropouts in MOOCs [C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 517-524.
 FEI M, YEUNG D Y. Temporal models for predicting student dropout in massive open online courses [C]// 2015 IEEE International Conference on Data Mining Workshop. IEEE, 2015: 256-263.
 YANG C, SHI X, JIE L, et al. I know you’ll be back: Interpretable new user clustering and churn prediction on a mobile social application [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 914-922.
 LU Y, YU L, CUI P, et al. Uncovering the co-driven mechanism of social and content links in user churn phenomena [C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 3093-3101.
 XIE Y, LI X, NGAI E W T, et al. Customer churn prediction using improved balanced random forests [J]. Expert Systems with Applications, 2009, 36(3): 5445-5449.
 WEI C P, CHIU I T. Turning telecommunications call details to churn prediction: A data mining approach [J]. Expert Systems with Applications, 2002, 23(2): 103-112.
 DASGUPTA K, SINGH R, VISWANATHAN B, et al. Social ties and their relevance to churn in mobile telecom networks [C]// Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology. 2008: 668-677.
 HUANG Y, ZHU F, YUAN M, et al. Telco churn prediction with big data [C]// Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015: 607-618.
 CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 785-794.
 BAI T, ZHANG S, EGLESTON B L, et al. Interpretable representation learning for healthcare via capturing disease progression through time [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 43-51.
 GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J]. Neural Networks, 2005, 18(5/6): 602-610.
 CHO K, VAN MERRIËNBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [EB/OL]. (2014-09-03) [2020-07-05]. https://arxiv.org/pdf/1406.1078v3.pdf.
 SRIVASTAVA N, MANSIMOV E, SALAKHUDINOV R. Unsupervised learning of video representations using LSTMs [C]// International Conference on Machine Learning. 2015: 843-852.
|||KUANG Jun, TANG Wei-hong, CHEN Lei-hui, CHEN Hui, ZENG Wei, DONG Qi-min, GAO Ming. Algorithm for video click-through rate prediction [J]. Journal of East China Normal University(Natural Sc, 2018, 2018(3): 77-87.|