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Predicting user churn and taking personalized measures to retain users is aset of common and effective practices for online game operators. However,different from the traditional user churn relevant researches that can involvedemographic, economic, and behavioral data, most online games can only obtainlogs of user behavior and have no access to users' latent feelings. There aremainly two challenges in this work: 1. The latent feelings, which cannot bedirectly observed in this work, need to be estimated and verified; 2. Userchurn needs to be predicted with only behavioral data. In this work, aRecurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, whichcan get the users' latent feelings while predicting user churn. Besides, weproposed a method named BMM-UCP (Behavior-based Modeling Method for User ChurnPrediction) to help models predict user churn with only behavioral data. Thelatent feelings are names as satisfaction and aspiration in this work. Wedesigned experiments on a real dataset and the results show that our methodsoutperform baselines and are more suitable for long-term sequential learning.The latent feelings learned are fully discussed and proven meaningful.

A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data