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    题名: Modeling player performance in massively multiplayer online role-playing games: The effects of diversity in mentoring network
    作者: Shim, K.J.;Hsu, Kuo-Wei;Srivastava, J.
    徐國偉
    贡献者: 資訊科學系
    关键词: Future performance;Game log;Game servers;Massively multi-player online games;Massively multiplayer;Mentoring;Performance metrics;Performance prediction;Player performance;Predictive models;Role-playing game;Sony Online Entertainment;Video game;Apprentices;Forecasting;Human computer interaction;Interactive computer graphics;Internet;Social networking (online)
    日期: 2011-07
    上传时间: 2015-10-08 17:51:16 (UTC+8)
    摘要: 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 EverQuest 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 EverQuest 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. © 2011 IEEE.
    關聯: Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, 論文編號 5992611,438-442
    数据类型: conference
    DOI 連結: http://dx.doi.org/10.1109/ASONAM.2011.113
    DOI: 10.1109/ASONAM.2011.113
    显示于类别:[資訊科學系] 會議論文

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