Customer Churn Prediction Model Based on User Behavior Sequences

• Published in: 东华大学学报（英文版） Vol. 39; no. 6; pp. 597 - 602
• Main Authors: ZHAI Cuiyan, ZHANG Manman, XIA Xiaoling, MIAO Yiwei, CHEN Hao
• Format: Journal Article
• Language: English
• Published: College of Computer Science and Technology,Donghua University,Shanghai 201620,China 2022
• Subjects: long-short term memory (LSTM); customer churn prediction; multi-headed attention mechanism
• ISSN: 1672-5220
• DOI: 10.19884/j.1672-5220.202202957

TP311; 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 future. Many 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 lacked. In this paper, a customer churn prediction model based on user behavior sequences is proposed. In this method, a long-short term memory (LSTM) network is introduced to learn the overall interest preferences of user behavior sequences. And 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 users. Experimentally validated on a real telecom dataset, the method has better prediction performance and further enhances the capability of the customer churn prediction system.