Departure prediction of online game players

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dc.contributor.authorSavetratanakaree, Kittipat
dc.contributor.authorSookhanaphibarn, Kingkarn
dc.contributor.authorIntakosum, Sarun
dc.contributor.authorThawonmas, Ruck
dc.contributor.authorChen, Kuan Ta
dc.date.accessioned2026-08-06T10:09:00Z
dc.date.available2026-08-06T10:09:00Z
dc.date.issued2014-01-01
dc.description.abstractMost business models of online game companies usually depend on sale of virtual items and the monthly subscription fees. The prediction of player departure could increase revenues by giving special promotions out to the players who are expected to unsubscribe or quit playing the game. This paper proposes a departure prediction approach by using a new feature called "SLKdays" and a game revisitation. The feature "SLKdays" is defined as "Staytime", which is the time each player spending in an online game, of the last k days, and a game revisitation is the playing frequency in the last month to predict the next month subscription. We explore our new feature "SLKdays" to determine the optimal number of k for the departure prediction. With our proposed feature, the accuracy of the departure prediction is high, 91.92%, and the precision and recall rate are 98.22% and 84.51%. © (2014) Trans Tech Publications, Switzerland.
dc.identifier.citationAdvanced Materials Research, 931-932, 1370-1374, 2014
dc.identifier.doi10.4028/www.scientific.net/AMR.931-932.1370
dc.identifier.issn10226680
dc.identifier.other2-s2.0-84901489315
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5537
dc.sourceAdvanced Materials Research
dc.subjectData mining
dc.subjectGame revisitations
dc.subjectMassively multiplayer online role-playing game(MMORPG)
dc.subjectPlayer behavior
dc.subjectShen zhou online
dc.subjectSLKdays
dc.subjectUser behaviors
dc.titleDeparture prediction of online game players
dc.typeConference Paper

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