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Trends In Game Mining: An Overview from Pure Telemetry Analysis to Deep Learning


Rafet Sifa (Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS)
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Understanding player behavior using data science tools has become a vital step in today’s agile game development cycle. Feedback received from such tools help developers and studios take immediate action to increase retention and monetization rates. Due to the tremendous growth in the size of game telemetry data and the level of complexity in player-game interactions, we require scalable, reliable and interpretable data analysis methods. In this talk we will give a use-case based overview of the challenges and the latest methodological trends in game mining putting emphasis on the role of classes of learning systems used and representation learning methods such as Deep Learning and Matrix Factorization in solving game business intelligence problems.
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Time slot: 
2016-04-19 12:00
Channel 5

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