상세정보
학술대회
Clustering Player Behavioral Data and Improving Performance of Churn Prediction from Mobile Game
- 발행일
- 201810
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1252-1254
- 협약과제
- 18CS1300, 지능형 라이브 서비스를 위한 게임 운영 시나리오 최적화 플랫폼 기술 개발, 양성일
- 초록
- Recognizing change of players' behavior is crucial as video game evolves. Therefore, clustering players' behavioral data has become an important issue. In this paper, we propose a clustering method from behavior log data. Using the proposed method, we present two experimental results: one is to analyze change of the clustered behavior pattern to in-game event and the other is to use the clustering result as a feature to improve a churn supervised learning model. Experimental results show that the proposed method has applied to analyze influence of in-game event on players' behavior as well as predict churn for real-world application.
- KSP 제안 키워드
- Clustering method, Learning model, Log data, Mobile Game, Real-world applications, Supervised Learning, behavior pattern, behavioral data, churn prediction, video games