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The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Yokozuna Data, a Keywords Studio, 102-0074 Tokyo, Japan
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Mach. Learn. Knowl. Extr. 2019, 1(1), 252-264;
Received: 1 November 2018 / Revised: 14 December 2018 / Accepted: 16 December 2018 / Published: 20 December 2018
(This article belongs to the Special Issue Women in Machine Learning 2018)
Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions—since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided—and efficient in terms of time cost. Thanks to these features and also to their ability to scale to large datasets, our models could be readily implemented in real business settings. View Full-Text
Keywords: churn; competition; video games; user behavior; behavioral data churn; competition; video games; user behavior; behavioral data
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Guitart, A.; Chen, P.P.; Periáñez, Á. The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition. Mach. Learn. Knowl. Extr. 2019, 1, 252-264.

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