Research And Application Of Telecom Customer Churn Prediction Based On Deep Learning

In recent years,the telecoms sector has become saturated with users and operators have focused on reducing churn to protect their overall user base in order to maintain business stability.In the face of an increasingly diverse range of telecoms users,researchers have used deep learning techniques to fully exploit the interests of users,and this approach has become one of the most important tools for predicting user departure in recent years.However,current research lacks a deeper understanding of long-term user-generated behavioral data and overall user intention preferences,and there is an even greater lack of research into modelling and analysis of basic user information.In this paper,two customer churn prediction models are proposed from the perspectives of user behavior sequence depth modeling and user behavior fusion user base information modeling respectively to address the above problems,at the same time,this paper has carried out experimental validation on a real operator dataset and designed and developed a customer churn prediction algorithm system.The main work in this paper is as follows:(1)The MALSTMN(Multi-head Attention and Long Short Term Memory Network)customer churn prediction model is based on user activity sequences.The behavioral data generated during a user’s time on the Internet can be very relevant to the user’s intentional preferences at different times,and whether or not the current user leaves the Internet is closely linked to the user’s previous behavior,so it is vital that the user’s behavioral data is adequately modelled.The MALSTMN model proposed in this paper uses long and short-term memory networks to analyze user sequence data to achieve prediction of time-series feature data and grasp the dynamic changes of user interests;for feature extraction,we introduce a multi-headed attention mechanism to learn the collaboration information between multiple user behaviors from multiple perspectives to achieve the capture of user behavior feature information,and obtain experimental validation on actual operator telecom user datasets.(2)A User Behavior fusion Base information Network(UBBN)is proposed as a predictive model for customer churn prediction.Through data exploration and analysis,it is found that the correlation between off-grid users and users to be predicted can be found by fully mining the basic information of users,so the proposed UBBN model adds the modelling of the basic information of users.The model uses attention mechanisms and cosine similarity to model user base information and measure the degree of match between users,while using LSTM networks to capture the long-term intentional preferences of user behavior.The model breaks away from the idea of using complex networks to model only historical user behavior data,and instead uses a basic time-series predictive network to model user behavior,investing more in mining correlations between off-grid users and currently on-grid users,increasing the parallelism of the model’s computation.Ultimately,experimental results with actual operator telecom user datasets show that user matching modelling also works well for customer churn prediction.(3)This paper designs and develops a customer churn prediction system.The system integrates the customer churn prediction model proposed in this paper with a basic time-series prediction model,and increases the efficiency of off-grid prediction by visualising the interface for displaying and tuning the prediction results.The two models proposed in this paper have been experimented on a real carrier telecom user dataset for several times,and the experimental results fully confirm the effectiveness and efficiency of the models.Ultimately,the proposed model has been put to use in a real production environment.