Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Author
Keywords
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.
Year of Publication
2016
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
3
Issue
Special Issue on Big Data and AI
Number
6
Number of Pages
44-51
Date Published
03/2016
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/files/2016/02/ijimai20163_6_7_pdf_23096.pdf
DOI
10.9781/ijimai.2016.367
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