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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35127

    Title: 運用分群演算法之關係式網頁推薦系統
    Other Titles: Concept-based page recommendation by using clustering algorithm
    Authors: 呂敏源;Lu, Ming-yuan
    Contributors: 淡江大學資訊工程學系碩士班
    郭經華;Kuo, Chin-hwa
    Keywords: 內容過濾;協同過濾;推薦系統;Content filtering;Collaborative Filtering;Recommendation System
    Date: 2007
    Issue Date: 2010-01-11 06:03:04 (UTC+8)
    Abstract: 如果將網際網路看成是目前資料量蘊藏量最大,資料提供者最多的一個資料庫,那麼如何去挖掘這麼龐大的資料庫,已經是近幾年來最熱門的研究議題,然而如何在廣大的資料庫中推薦給使用者合適的網頁,隨著推薦演算法相關研究中目前可以分成以內容導向,以及協同過濾為主。但是各有其缺點。本論文主要目的為探討如何結合內容導向以及協同過濾的優點,並且藉由分群演算法來改善以往推薦演算法因為使用者及推薦項目的增加,讓推薦計算的時間呈倍數成長的缺點,並且利用叢聚係數以提高推薦系統的可信度。 在本研究中,利用了代理伺服器來搜集使用者瀏覽網路的資訊,並且透過代理伺服器所記錄的存取記錄表單來建立使用者的瀏覽行為。在使用者搜集子系統最後,將利用存取表單中的網址重新抓取使用者所瀏覽過的內容。透過文章內容前處理系統,利用內容導向的觀念以擷取關鍵字的方法來得到文章特徵的描述,經過過濾不重要的關鍵字,讓文章的焦點集中在文章的主題上。透過推薦子系統利用階層式分群法將網頁分群,利用協同過濾的方式計算使用者在群組裡的推薦項目。
    This paper intends to exploit the idea of sharing to design a method different from common recommendation system; we use the concept of user-to-user recommendation system. Using a grouping method, the user can receive groups of high interest and other users’ related browsing groups.
    In this research, we used a proxy server to search for information related to the user’s browsed webpages. From the records of the proxy server we construct a profile of the user’s browsing habits. At the end of the user’s search subsystem, we will use content based concept to extract keywords to obtain the article’s characteristics’ description. Unwanted keywords are filtered, so that the article’s focus is on the topic itself. From the recommendation system, the webpages will be classified using the hierarchical grouping method, and through collaborative filtering, the recommended webpages will be chosen.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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