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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/54176

    Title: Online web recommendation system by using stochastic timed Petri nets
    Other Titles: 使用隨機派翠網路之線上網頁推薦系統
    Authors: 孫初豪;Sun, Chu-Hao
    Contributors: 淡江大學資訊工程學系博士班
    Keywords: 網頁推薦系統;網頁使用習性探勘;資料串流;隨機過程時間派翠網路;網頁結構特性;衡量網站的基準;Web Usage Mining;Data stream mining;Stochastic Timed Petri Nets;Markov model;Web graph properties;Web metrics
    Date: 2011
    Issue Date: 2011-06-16 22:06:52 (UTC+8)
    Abstract: 網頁推薦系統為網頁使用習性探勘典型的應用,而網頁推薦系統在結構上可分成離線(off-line)與線上(online)兩個元件,離線元件主要根據分析過去歷史使用者習性資料(usage profile)來建立知識,並提供給線上元件使用。由於一般網頁推薦系統大多使用離線之資料前置處理,而執行資料探勘時也沒有時間限制,故此方法並不適合即時的動態環境,因此,我們需要高效能之線上網站使用習性探勘技術來解決這些問題。
    我們在本文中提出以隨機過程時間派翠網路為基礎之線上網頁推薦系統,網頁推薦處理程序可分為資料準備、模式發掘和推薦三個階段。在資料準備階段,我們利用隨機過程時間派翠網路作為分析網站網頁架構的模型,並加入衡量網站基準分析網頁結構特性來增進網頁資訊的擷取。而分析網站架構後所得到的關聯矩陣與派翠網路模型之可到達行為特性可協助進行資料前置處理中的網頁內容範圍辨識和路徑填補。在模式發掘階段,我們應用流覽圖(navigational graph)和可到達圖(reachability graph)來作為使用者習性資料的模型。我們利用資料串流探勘技術維護在滑動視窗(sliding-window)上的流覽圖,並應用關聯矩陣與派翠網路模型之可到達行為特性進行可到達圖的建立。在推薦階段,我們使用馬可夫移轉機率和穩定狀態機率來協助預測使用者網頁瀏覽的行為,推薦引擎可藉由模式發掘階段所產生之流覽圖和可到達圖來協助完成動態網頁的推薦。而我們結合以代理人為基礎和事件驅動非同步通知的架構來達到線上即時的資料準備、模式發掘和推薦三個階段。
    Web recommendation systems are typical applications of Web usage mining. The Web recommendation system is structured according to an online and an off-line component. The off-line component is aimed at building the knowledge base by analyzing past usage profiles that is then used in the online component. The general Web recommendation system mainly uses offline data preprocessing and the mining process is not time-limited. However, this approach is not suitable in real-time dynamic environments. Therefore, we need high-performance online Web usage mining techniques to provide solutions to these problems.
    In this paper, we propose an online Web recommendation system using STPN. The Web recommendation process consists of the data preparation phase, pattern discovery phase, and recommendation phase. In data preparation phase, we use STPN to model the Web structure, and also apply the Web metrics of Web graph properties to analyze the Web structure in improving the Web access. We simultaneously employ the Web structure analysis information in the incidence matrix and the reachability properties, obtained from the STPN model, to help proceed with pageview identification and path completion at the data preprocessing. In pattern discovery phase, the navigational graph and reachability graph are employed to model the usage profiles. We use the data stream mining technique to incrementally maintain the navigational graph over a sliding-window. The STPN’s reachability behavior characteristic and incidence matrix are applied to construct the reachability graph. In recommendation phase, we use the transition probability and steady-state probability in Markov model to predict the user’s navigational behavior. The navigational graph and reachability graph are used by the Web recommendation engine to generate the online dynamic Web recommendation. We combine the features of both the agent-based architecture and event-driven asynchronous notification architecture to achieve the online data preparation, pattern discovery, and recommendation.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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