在廣大的網際網路中要找尋到自己真正想要的資訊通常不是一件很輕鬆的事，有鑑於此為了改善使用者運用系統服務的品質，本論文提出一套方法利用使用者瀏覽於網際網路上的行為，分析並學習使用者的喜好及習慣，以建置個人知識地圖(Personal Ontology)，藉此讓電腦系統對於使用者有所瞭解，進而提升系統服務品質。本論文首先分析使用者於網際網路上之行為記錄，利用聚類、資料探勘及模糊推論等技術，找出使用者正常瀏覽行為。然後利用中研院所提供之CKIP中文斷詞系統，將屬於使用者正常瀏覽行為的內容進行斷詞及詞性標注，再利用此內容的領域知識地圖(Domain Ontology)進行內容概念比對，再參照使用者瀏覽順序和次數等特徵並以模糊推論計算出使用者對於此份內容裡所描述概念的喜好程度，然後對應到領域知識地圖中的概念，給予個人化的權重，以呈現使用者對於此領域的喜好程度，進而形成一個個人化的知識地圖。最後，本論文提出一個資訊推薦機制，運用個人知識地圖進行概念比對，為使用者推薦其喜好相關之內容。

To find the information that we do really want in the large-scale Internet isn’t a light work. Hence, in order to improve the quality of the information service, we propose a model that according to the user’s browsing behavior on the Internet to analyze and learn the user’s preference and habits, and construct personal ontology for the specific domain. First, we analyze the user’s behavior on the Internet by the technologies of clustering, data mining, and fuzzy inference to find out the user’s normal browsing behavior. Second, the CKIP system is utilized to carry out the Chinese natural language processing for the user-browsed contents. Third, the concepts of content are derived from the browsed content and the domain ontology. In addition, the features of user browsing sequence and browsing time are used to infer the user’s preference degree by fuzzy inference mechanism. Finally, we propose an information recommendation model to recommend the information that related with the preference of the user with personal ontology.

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