Generative AI in Mafia-Like Game Simulation

Jan 8, 2024 ·

This arXiv paper introduces a novel approach to the prediction of user churn in mobile applications. The authors propose a two-stage model to predict user churn, which combines both traditional machine learning models and deep learning models.

The first stage of the proposed model uses traditional machine learning models such as logistic regression and support vector machines to extract relevant features from the data. These features are then used by a deep learning model for further analysis. Specifically, the deep learning model consists of an attention mechanism and a recurrent neural network (RNN). The attention mechanism extracts important features from the user data, while the RNN captures temporal information from the user’s interactions with the application.

The authors also introduce a new metric called “Stability Score” that quantifies how stable a user’s usage pattern is over time. This metric can be used as a measure of user engagement, which can be used to identify users who are likely to churn. The authors evaluate their method on a real-world dataset collected from a popular mobile application.

Overall, this paper proposes a novel two-stage model for predicting user churn in mobile applications. The proposed model combines traditional machine learning models with deep learning models to identify important features from user data. Additionally, the authors introduce a new metric for measuring user engagement and demonstrate its effectiveness through experiments on a real-world dataset.