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Conference Paper
Predicting Churn in Mobile Free-to-Play Games
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- Authors
- Sang-Kwang Lee, Seung-Jin Hong, Seong-Il Yang, Hunjoo Lee
- Issue Date
- 2016-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2016, pp.1046-1048
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In the mobile game industry, Free-To-Play games are dominantly released, and therefore player retention and purchases have become important issues. In this paper, we propose a game player model for predicting when players will leave a game. Firstly, we define player churn in the game and extract features that contain the properties of the player churn from the player logs. And then we tackle the problem of imbalanced datasets. Finally, we exploit classification algorithms from machine learning and evaluate the performance of the proposed prediction model using cross-validation. Experimental results show that the proposed model has high accuracy enough to predict churn for real-world application.
- KSP Keywords
- Classification algorithm, Cross validation(CV), Free-To-Play, Game industry, Game player, High accuracy, Mobile Game, Proposed model, Real-world applications, extract features, imbalanced dataset