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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00218995%21RIV15-MSM-21230___
rdf:type
n6:Vysledek skos:Concept
rdfs:seeAlso
http://ebooks.iospress.nl/volumearticle/37212
dcterms:description
This paper focuses on predicting player behaviour in two-player games with microtransactions. Typically the games are for free and companies generate their revenue by selling in-game goods. We show creation of a users behaviour model, which are then used in a recommendation system increasing in-game goods purchases. We focus on learning techniques in a novel way, predicting the time of purchases rather than the most likely product to be purchased. The player model is based on in-game signals, such as players success, curiosity, social interactions etc. We had access to a Pool Live Tour game dataset made by Geewa. We report promising results in predicting the purchase events. This paper focuses on predicting player behaviour in two-player games with microtransactions. Typically the games are for free and companies generate their revenue by selling in-game goods. We show creation of a users behaviour model, which are then used in a recommendation system increasing in-game goods purchases. We focus on learning techniques in a novel way, predicting the time of purchases rather than the most likely product to be purchased. The player model is based on in-game signals, such as players success, curiosity, social interactions etc. We had access to a Pool Live Tour game dataset made by Geewa. We report promising results in predicting the purchase events.
dcterms:title
Predicting Players Behavior in Games with Microtransactions Predicting Players Behavior in Games with Microtransactions
skos:prefLabel
Predicting Players Behavior in Games with Microtransactions Predicting Players Behavior in Games with Microtransactions
skos:notation
RIV/68407700:21230/14:00218995!RIV15-MSM-21230___
n3:aktivita
n20:S
n3:aktivity
S
n3:dodaniDat
n14:2015
n3:domaciTvurceVysledku
n18:3887391 n18:7871236
n3:druhVysledku
n16:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
38737
n3:idVysledku
RIV/68407700:21230/14:00218995
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Machine Learning; Feature Extraction; Online Games; Data Mining
n3:klicoveSlovo
n5:Feature%20Extraction n5:Online%20Games n5:Data%20Mining n5:Machine%20Learning
n3:kontrolniKodProRIV
[84A419583B9C]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Amsterdam
n3:nazevZdroje
Frontiers in Artificial Intelligence and Applications
n3:obor
n22:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n14:2014
n3:tvurceVysledku
Šedivý, Jan Pluskal, Ondřej
n3:typAkce
n13:EUR
n3:wos
000350218400024
n3:zahajeniAkce
2014-08-18+02:00
s:issn
0922-6389
s:numberOfPages
10
n8:doi
10.3233/978-1-61499-421-3-230
n21:hasPublisher
IOS Press
n9:isbn
978-1-61499-420-6
n12:organizacniJednotka
21230