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Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and(More)
The market of massively multiplayer online games (MMOGs) is expanding rapidly. MMOG business can be considered as eBusiness where CRM (Customer Relationship Management) is a key factor of success. This paper discusses an effective approach for player identification , the results of which can be exploited for CRM of MMOGs. A feature extraction method is(More)
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