政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/74404
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113318/144297 (79%)
造訪人次 : 51080993      線上人數 : 961
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/74404


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/74404


    題名: Churn prediction in MMORPGs using player motivation theories and an ensemble approach
    作者: Borbora, Z.;Srivastava, J.;Hsu, Kuo-Wei;Iams, D.W.
    徐國偉
    貢獻者: 資科系
    關鍵詞: Clustering techniques;Customer retention;Data sets;Data-driven;Data-driven approach;Data-driven model;Domain experts;Interpretability;Lift analysis;Massively multiplayer;Mmorpgs;Model complexity;Prediction accuracy;Prediction model;Prediction problem;Return on investments;Role-playing game;Customer satisfaction;Mathematical models;Motivation;Sales;Social networking (online);Social sciences computing;Forecasting
    日期: 2011-10
    上傳時間: 2015-04-08 17:33:48 (UTC+8)
    摘要: In this paper, we investigate the problem of churn prediction in Massively multiplayer online role-playing games (MMORPGs) from a social science perspective and develop models incorporating theories of player motivation. The ability to predict player churn can be a valuable resource to game developers designing customer retention strategies. The results from our theory-driven model significantly outperform a diffusion-based churn prediction model on the same dataset. We describe the synthesis between a theory-driven approach and a data-driven approach to a problem and examine the trade-offs involved between the two approaches in terms of prediction accuracy, interpretability and model complexity. We observe that even though the theory-driven model is not as accurate as the data-driven one, the theory-driven model itself can be more interpretable to the domain experts and hence, more preferable over a complex data-driven model. We perform lift analysis of the two models and find that if a marketing effort is restricted in the number of customers it can contact, the theory-driven model would offer much better return-on-investment by identifying more customers among that restricted set who have the highest probability of churn. Finally, we use a clustering technique to partition the dataset and then build an ensemble on the partitioned dataset for better performance. Experiment results show that the ensemble performs notably better than the single classifier in terms of its recall value, which is a highly desirable property in the churn prediction problem. © 2011 IEEE.
    關聯: Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, 論文編號 6113108, 157-164
    10.1109/PASSAT/SocialCom.2011.122
    資料類型: conference
    DOI 連結: http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.122
    DOI: 10.1109/PASSAT/SocialCom.2011.122
    顯示於類別:[資訊科學系] 會議論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML21085檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    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 ©   - 回饋