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- W2319314015 abstract "This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user�s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies." @default.
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- W2319314015 date "2016-01-01" @default.
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- W2319314015 title "Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study" @default.
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- W2319314015 doi "https://doi.org/10.9781/ijimai.2016.367" @default.
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