Paper Info
Title
Sequence Alignment Based Analysis of Player Behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs)
Abstract
This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following key components: 1) sequence alignment-based player profile databases, 2) feature selection schemes and prediction model building, and 3) decision support model for determining inactive players.
Year
DOI
Venue
2010
10.1109/ICDMW.2010.166
ICDM Workshops
Keywords
DocType
ISBN
unique scoring scheme,massively multiplayer online role-playing games,local sequence alignment algorithms,massively multiplayer online role-playing,activity sequence,inactive players,global sequence alignment algorithms,sequence alignment,feature selection schemes,sony online entertainment,prediction model building,player behavior,inactivity,activity sequences,inactive game players prediction,inactive player,statistical databases,decision support systems,active player,sequence similarity scores,sequence similarity score,game player activity data,sequence alignment-based behavior analysis,inactive game player,local sequence alignment algorithm,user behavior,derived statistics form profile databases,active players,sequence alignment-based player profile,everquest ii,games,decision support model,computer games,prediction model,decision support,support vector machines,databases,feature selection,behavior analysis,kernel,prediction algorithms,predictive models
Conference
978-0-7695-4257-7
Citations 
PageRank 
References 
1
0.36
13
Authors
2
Name
Order
Citations
PageRank
Kyong Jin Shim16713.91
Jaideep Srivastava25845871.63