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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68822

    Title: 探討多重記憶系統應用於遺忘因子的使用者興趣模型
    Authors: 蘇鼎文;Su,Ting-Wen
    Contributors: 資訊管理學系
    Keywords: 多重記憶系統模型;使用者模型;遺忘因子;文件過濾;圖形居中;Atkinson–Shiffrin memory model;User Profile;Forgetting Factor;Document Filter;Graph Centrality
    Date: 2015-07-27
    Issue Date: 2015-09-23 14:42:50 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著使用者的閱讀習慣從紙本轉成數位、電腦轉成手機平板,使得使用者能夠隨時隨地的閱讀,不僅也增加了平均閱讀量也造成了更容易分散注意力的環境,面對這些新的挑戰,系統除了需要解決使用者興趣的概念飄移的問題以外還需要解決因網路資料規模呈指數成長而所造成系統處理即時性的問題。
    ;While user’s channels of reading is changing from physical to digital, desktop computer to mobile device. It becomes easier for user to read at anywhere, anytime. It have not only increasing the amount of average reading but also causing the user interest drift more often. To solve these problems, information filter system have to adapt the concept drift of user interests and trains fast enough to deal with the explosion of documents streaming.
    The research try to use different centrality algorithm to find the core set of keywords in user profile′s graph. Using the strong keywords instead of all of the keywords in the graph, system improves the speed of building user profile and even the performance of the system. In addition, the research design the user profile′s interest base on the Atkinson-Shiffrin′s multi-store model, the framework divided user interests into long-term interest and short-term interest. The short-term interest use the dynamic forgetting factor to adapt the concept drift occurred in user profile. In contrast, the long-term interest using the static forgetting factor to store information for the system to use. the experiments proved short term forgetting factor can adapt the concept drift quicker, and the long term forgetting factor can save more information in the interest. In the end, research’s system shows better F-measure performance and more efficient than the other research.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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