近年來許多學者針對網頁的推薦系統進行相關研究，透過網頁所蘊含的資訊，幫助使用者過濾龐大的網頁資料量，讓使用者能夠快速地找到欲查詢的網頁資料。本論文結合最近鄰居演算法與K平均演算法於推薦系統之研究，研究目的為，推薦使用者其感興趣的網頁，與提升使用者瀏覽時的便利程度。我們的推薦系統將分析使用者目前正在瀏覽的網頁之所屬網站，以推薦額外的網頁給使用者。此系統結合瀏覽器對使用者進行推薦，讓使用者能夠直接在網頁上點擊系統所推薦的網頁進行瀏覽。本研究針對使用者目前瀏覽的網頁，進行網頁資訊的收集。主要透過已收集的網頁，使用中文斷詞系統分析每一個網頁的標題和內容，並且採用TF-IDF計算關鍵字等資訊，再藉由KNN-means演算法進行分群，以關鍵字雲(Tag Cloud)的方式呈現予使用者，讓使用者能夠快速地找到感興趣的網頁進行瀏覽。

In recent years, many researchers focus on web recommendation systems, which filter a lot of web pages, in order to let users find desired web page information quickly.
We proposed a recommendation system which combines with K-nearest neighbor algorithm and K-means clustering algorithm. When the user browses the current web page, our system will recommend additionally interesting web pages to the user after our system analyze the current website. There are four processing phases in our recommendation system. First, we collected the website that user current browsed. Second, we used the CKIP (Chinese knowledge Information Processing Group) system to analyze every web page titles and contents. Then we compute keywords information by TF-IDF. Third, we propose KNN-means to cluster our web pages. Forth, we show tag-cloud as our clustered result.

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