English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109948/140897 (78%)
Visitors : 46093400      Online Users : 1021
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/78915
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/78915


    Title: Modeling player performance in massively multiplayer online role-playing games: The effects of diversity in mentoring network
    Authors: Shim, K.J.;Hsu, Kuo-Wei;Srivastava, J.
    徐國偉
    Contributors: 資訊科學系
    Keywords: 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)
    Date: 2011-07
    Issue Date: 2015-10-08 17:51:16 (UTC+8)
    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 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.
    Relation: Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, 論文編號 5992611,438-442
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/ASONAM.2011.113
    DOI: 10.1109/ASONAM.2011.113
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML2685View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback