Paper Info
Title
Modeling Player Performance in Massively Multiplayer Online Role-Playing Games: The Effects of Diversity in Mentoring Network
Abstract
This study investigates and reports preliminary findings on player performance prediction approaches which model player's past performance and social diversity in mentoring network in Ever Quest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. Our contributions include a better understanding of performance metrics used in the game and a foundation of recommendation systems for mentors and apprentices. We examined three different game servers from the Ever Quest II game logs. In all three servers, the results from our analyses suggest that increase in social diversity in terms of characters and classes encountered moderately negatively correlates with player performance. Based on this finding, we built predictive models to predict player's future performance based on past performance and social diversity in terms of mentoring activities. Our results indicate that 1) models employing past performance and social diversity perform better and 2) prediction for mentors is generally better than that for apprentices.
Year
DOI
Venue
2011
10.1109/ASONAM.2011.113
ASONAM
Keywords
Field
DocType
massively multiplayer online role-playing,model player,massively multiplayer online role-playing game,player performance prediction approach,player performance modeling,everquest ii game logs,mentoring,different game server,modeling player performance,better understanding,player performance prediction,social diversity,quest ii game log,mmorpg,video games,massively multiplayer online games,recommender systems,past performance,mentoring network,entertainment,internet,sony online entertainment,player performance,recommendation system,social networking (online),future performance,performance metrics,computer games,linear regression,virtual environment,statistical analysis,social participation,human computer interaction,recommender system,predictive models,performance management,servers,bagging,apprentices,cultural differences,social interaction,forecasting,prediction model,games
Recommender system,Apprenticeship,Computer science,Entertainment,Server,Cultural diversity,Performance prediction,Multimedia,Role playing,The Internet
Conference
ISBN
Citations 
PageRank 
978-0-7695-4375-8
2
0.48
References 
Authors
4
3
Name
Order
Citations
PageRank
Kyong Jin Shim16713.91
Kuo-Wei Hsu2536.38
Jaideep Srivastava35845871.63