Churn Prediction Algorithms for Telecom Industry Based on Attention Mechanism

• 1、School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing 100876

Abstract：With the slowdown of user growth in the telecommunication market, the competition in the telecommunication market tends to be white-hot, and it is increasingly common for telecom users to change operators. The cost of acquiring new users outweighs the cost of retrieving potential churn users. Therefore, with the help of large data and deep learning methods to locate and retain potential churn users in advance has become an important way for operators to maintain the number of users and ensure business income. In this paper, a churn prediction method based on deep neural network model with call detail record (CDR) data is presented. This paper first extracts basic communication behavior features, the social features of tele communication and the location-based features. Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are used to learn the dependencies and temporal relatioships among user features. By introducing attention mechanism and utilizing the different influence of user location-based features on other features, this paper improves the final churn prediction performance by learning the weights of various features. Finally experiments on operator CDR data show that the performance of two prediction models based on attention mechanism is better than traditional machine learning algorithms. .

Keywords： Artificial Intelligence Operator Data Churn Prediction Deep Learning Attention