상세정보
학술대회
Predicting Churn in Mobile Free-to-Play Games
- 발행일
- 201610
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2016, pp.1046-1048
- 협약과제
- 16CS1600, 지능형 라이브 서비스를 위한 게임 운영 시나리오 최적화 플랫폼 기술 개발, 양성일
- 초록
- 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 제안 키워드
- 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