A Latent Feelings-aware RNN Model for User Churn Prediction with only Behaviour data

User Churn Prediction is a cutting-edge research area in the web service industry, it is the key for managing the user in the virtual world and provide feedback information for improving the corresponding web service. At present, most of the relevant work is to design a questionnaire to collect data of users' characteristics and feelings and then develop a general model by finding relevance. However, that kind of methods requires quite a time and manpower, and most web services can only obtain logs of users' behaviours and have no access to users' feature data. Therefore, it is a big challenge to conduct user churn prediction with only behavior data and get users' latent feelings from their action data in order to improve the accuracy of churn prediction. In this paper, a novel Latent Feelings-aware RNN model, namely LaFee, has been proposed to solve the user churn prediction problem by using only behaviour data. The latent feelings, proven to be satisfaction and aspiration, can be estimated through the intermediate variable of the trained LaFee. We also designed experiments on a real dataset and the results show that our methods outperform the baselines.