Customer Churn Prediction Model Based on User Behavior Sequences

作     者：ZHAI Cuiyan ZHANG Manman XIA Xiaoling MIAO Yiwei CHEN Hao 翟翠艳;张嫚嫚;夏小玲;缪艺玮;陈豪

作者机构：College of Computer Science and TechnologyDonghua UniversityShanghai 201620China 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2022年第39卷第6期

页      面：597-602页

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：multi-headed attention mechanism long-short term memory(LSTM) customer churn prediction 

摘      要：Customer churn prediction model refers to a certain algorithm model that can predict in advance whether the current subscriber will terminate the contract with the current operator in the *** scholars currently introduce different depth models for customer churn prediction research,but deep modeling research on the features of historical behavior sequences generated by users over time is *** this paper,a customer churn prediction model based on user behavior sequences is *** this method,a long-short term memory(LSTM)network is introduced to learn the overall interest preferences of user behavior *** the multi-headed attention mechanism is used to learn the collaborative information between multiple behaviors of users from multiple perspectives and to carry out the capture of information about various features of *** validated on a real telecom dataset,the method has better prediction performance and further enhances the capability of the customer churn prediction system.