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Conference Paper Clustering Player Behavioral Data and Improving Performance of Churn Prediction from Mobile Game
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Authors
Hyoungjin Kwon, Wooyoung Jeong, Dae-Wook Kim, Seong-Il Yang
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1252-1254
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539514
Abstract
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 Keywords
Clustering method, Learning model, Log data, Mobile Game, Real-world applications, Supervised Learning, behavior pattern, behavioral data, churn prediction, video games