全球資訊網是網際網路服務中使用最為簡便而且普及的服務，隨著網頁數目的快速增加，使用者在全球資訊網上的資訊檢索變得越來越困難，而搜尋引擎的使用是解決此問題的方法之一，然而，搜尋引擎所回饋的搜尋結果，常常多達數十筆甚至數百筆資料，使得使用者必須再花時間從其中找到其真正想要的網頁，另外，每個使用者因為年齡、學歷、興趣、專長等個人背景因素的不同，對於相同查詢字串檢索出來的查詢結果，可能會有不同的滿意度，因此，針對搜尋結果進行個人化的排序變成了一個重要的課題。
本研究提出利用乏晰概念網路（Fuzzy concept network）來協助使用者進行個人化網頁排序的方法，利用乏晰概念網路中的概念，紀錄使用者的興趣與偏好等個人化資料，然後根據乏晰概念網路的規則，計算搜尋結果中每個網頁與所有概念的關聯程度，最後依據關聯程度值的高低來重新排列搜尋結果的網頁。本研究另設計了一自動學習機制，藉由使用者對排序結果的回饋，能夠自動地修正使用者的個人化資料，使得排序結果越來越符合使用者的需求。

World Wide Web is the most common service in internet. As the number of homepage grows rapidly, it’s more and more difficult to retrieve information on WWW. The use of search engine is one way to solve this problem. However, the search result still contains numerous pages, and user often should pay much time to find the pages they really need from the search result. Besides, because of the different background, everyone may have different satisfaction at the same rank. For this reason, personalization is required for search result.
This paper present a method that uses the fuzzy concept network topersonalize the search result. The fuzzy concept network based on user profile reorders the search result and the system provides personalized high-quality result. We also propose a learning method. By analysing the feedback from users, the system can update user profile automatically.

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