個人化網站搜尋從以前到現在都是一個熱門的研究主題。很多搜尋引擎為了幫助使用者在大量資訊中找到他們所需要的搜尋結果而提供個人化搜尋的功能。在本研究中，主要的概念是透過使用搜尋引擎查詢記錄來了解查詢關鍵字與網址之間點擊關係背後的使用者搜尋意圖，接著根據使用者意圖實行個人化網頁排序。所以我們提出了一個整合查詢關鍵字分類、網址聚類分析以及使用者點擊記錄分析的推薦方法，從過去的搜尋記錄來了解使用者搜尋意圖，再與使用者目前的搜尋模式產生連結，進而產生個人化的網頁推薦。我們的實驗結果顯示，我們方法可以有效改善個人化網頁排序的效能，並可以提供更精確的推薦方式來幫助使用者更快地得到他們所需要的資訊。

Personalized web search is an active and popular research topic for a long time. Many search engines provide personalized search for helping users get their own required results within a large quantity of information. In this paper, the main concept is that through understanding users search intentions behind the click relationship between queries and URLs from search engine search log, and then doing the personalized ranking of web pages with users own search intention. So we introduce a suggestion approach that integrate three components: query classification, URL clustering, and user click history analysis. Finally, by comprehending the user intention from the search log, personalized suggestions are provided to the current users search pattern and web pages would be ranked for the user intention. The experimental results demonstrates our approach can effectively improve the performance of personalized ranking well and provide more accurate suggestion to help users get their required information.

1 Introduction 1
2 RelatedWork 4
3 Overview 7
4 Offline Processing 9
5 Online Suggestion Generation 16
6 Experiment 20
7 Conclusion 27
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