網際網路的環境中蘊含了豐富且大量的資訊，因此我們很難從其中找到真正想要的資料。在本篇論文中，我們提出了在網際網路環境中提供個人化推薦的方法。並介紹從使用者瀏覽紀錄所含的分類資料找出使用者的興趣與行為的概念。此外，我們也提出了一個探勘使用者興趣與行為的方法。根據每個使用者的興趣與行為，可以將其分成六大類。依據使用者的興趣與行為，我們也定義出使用者之間的距離。然後由距離的度量便可以將每一類的使用者再進一步做分群，找出相似的使用者。另外在推薦的部分則提出了四種推薦的方法。最後，我們利用實驗來評估所提出方法的效率。隨著近年來網際網路快速的發展，在網路上有著大量的資訊可供查詢。然而我們卻很難找到真正想要的資料。因此需要好的工具與方法來幫助使用者查詢資料。搜尋引擎與網站提供的個人化推薦服務是解決此問題常見的工具與方法。隨著電子商務的快速發展，找出網站上消費者的興趣並適當的推薦其感興趣的商品變成了一個很重要的研究課題。
在本篇論文中，我們提出了從使用者的瀏覽紀錄擷取出興趣與行為的概念。並且提出一個新的方法來探勘出使用者的興趣與行為。我們利用I-B Diagram來描述使用者的興趣與行為。由於每個使用者的I-B Diagram都不相同，據此我們將使用者分成六大類。每個I-B Diagram 會被轉換成一個相對應的矩陣。最後再轉換成I-B Vector以便計算不同使用者之間距離。利用算出來的距離可以對同一類的使用者做進一步的分群。利用分群的結果可以找出相似興趣與行為的使用者。最後，透過相似使用者的興趣與行為對使用者做出適當的推薦。

As the World Wide Web becomes an important information source in recent years, it is hard to get useful knowledge from the sea of information. In this paper, we describe a technique for making personalized recommendation on the Internet environment. We introduce the concepts of the user interest and behavior that are generated from the categories of documents read by the users. A new method for mining the user interest and behavior from the documents read by the user is proposed. According to the patterns of user interests and behaviors, we define six types of web users and describe a similarity measure of the patterns to classify the users into clusters. In addition, four kinds of recommendations based on the clustering results are provided for the users. Finally, we make several experiments to evaluate the effectiveness and efficiency of our approach.

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