Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study
Autor:
Baldominos, Alejandro
Albacete, Esperanza
Marrero, Ignacio
Saez, Yago
Fecha:
03/2016Palabra clave:
Tipo de Ítem:
Articulo Revista IndexadaDirección web:
https://ijimai.org/journal/bibcite/reference/2537
Resumen:
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.
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