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Conference Paper
Social Link Prediction and Feature Analysis in Mobile Game
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- Authors
- Dae-Wook Kim, Hyoungjin Kwon, Sang-Kwang Lee, Wooyoung Jeong, Seong-il Yang
- Issue Date
- 2018-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.906-909
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Predicting, monitoring, and analyzing player's behavior is essential for free-to-play mobile game business. Especially, game company have made their game with social features in order to prevent users from leaving. In this paper, we predict social relationship from game data and analyze factors affected to prediction accuracy by features, prediction period, machine learning algorithm, and social properties. The experiment is performed on commercial mobile game. Random forest shows the best performance. Psychological factors have small effect on link prediction and last 7 days within training period affects the most of future relationship.
- KSP Keywords
- Best performance, Feature Analysis, Free-To-Play, Link prediction, Machine Learning Algorithms, Mobile Game, Prediction accuracy, Prediction period, Psychological factors, Random forest, Social Properties