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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/17389


    Title: A Trend-based Prediction System for Web User Behavior
    Authors: Jan, Nien-yi;Lin, Shun-chieh;Lin, Nancy P.;Chang, Chung-I
    Contributors: 淡江大學資訊工程學系;軍訓室
    Keywords: Trend similarity;Prediction system;Web mining;User behavior;Sequence mining
    Date: 2008-01
    Issue Date: 2013-06-07 10:45:10 (UTC+8)
    Publisher: Athens: World Scientific and Engineering Academy and Society
    Abstract: Since web applications make great progress, the latency of Internet owing to the network bandwidth becomes an urge problem in the cyber world. It is very important to deliberate on how to construct a prediction model to predict web users traveling path for adapting the website structure and improving the website performance. A trend based prediction model without extra information is proposed in this paper to generate prediction models with a sequence of pages for a proxy server prefetcting the suitable pages. The trend similarity is the core of our proposed model which considers not only the page similarity but also position similarity. Two measures include page correctness rate and order correctness rate are proposed to evaluate accuracy of our prediction system.
    Relation: WSEAS Transactions on Advances in Engineering Education 5(1), pp.52-59
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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