A Latent Feelings-aware RNN Model for User Churn Prediction with
  Behavioral Data
  Predicting user churn and taking personalized measures to retain users is a
set of common and effective practices for online game operators. However,
different from the traditional user churn relevant researches that can involve
demographic, economic, and behavioral data, most online games can only obtain
logs of user behavior and have no access to users' latent feelings. There are
mainly two challenges in this work: 1. The latent feelings, which cannot be
directly observed in this work, need to be estimated and verified; 2. User
churn needs to be predicted with only behavioral data. In this work, a
Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which
can get the users' latent feelings while predicting user churn. Besides, we
proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn
Prediction) to help models predict user churn with only behavioral data. The
latent feelings are names as satisfaction and aspiration in this work. We
designed experiments on a real dataset and the results show that our methods
outperform baselines and are more suitable for long-term sequential learning.
The latent feelings learned are fully discussed and proven meaningful.
