網路搜尋引擎可以幫助使用者在全球資訊網中(WWW)找到其所需的資訊，然而它們通常是設計來為所有使用者提供服務，並未考慮到個別使用者喜好，在多數情況下搜尋引擎回傳的結果中會滲雜著使用者不感興趣的資料，使用者必須花費額外時間做過濾資訊的動作。相反地，個人化網頁搜尋則是依據使用者喜好來提供客製化的搜尋結果，幫助使用者在資訊尋求的過程中節省許多的時間。
在本論文中，我們提出之個人化網路搜尋的方法是將搜尋引擎回傳的結果依據使用者個別的喜好重新排序，幫助使用者在面對網路上大量資料加速其搜尋的速度。我們的方法是從使用者感興趣的網頁中萃取出使用者喜好的資訊，不需使用者的介入。當使用者提交查詢詞時，從搜尋引擎擷取搜尋結果，利用主題詞彙群組將搜尋結果分成多個具有不同主題的群組。然後利用使用者個人檔，評估使用者對於每個群組的喜好度，依據群組喜好度將網頁重新排序。實驗結果顯示我們的個人化網路搜尋方法可以有效提升搜尋的精確率。

Web search engines help users find useful information on the World Wide Web (WWW). However, they are usually designed to serve all users, without considering the preference of individual users. The results returned from search engines are peppered with many impertinent data under most situations, and therefore users must pay a lot of effort to filter information. On the contrary, personalized web search provides customized results depending on each user’s preference, which can save a lot of time for users.
In the thesis, we propose a method for personalizing web search by reranking search engine results based on the preferences of each individual user, which can greatly aid the search when facing massive amounts of data on the internet. Our method first extracts information of users’ preferences from their interested web pages without any involvement of users. When a query is submitted, we retrieve results from a search engine and cluster them into several topics using topic keyword clusters. Then rerank web pages by topic preference which is evaluated from user profile. Experimental results reveal that our personalized web search method can effectively enhance the precision rate of the retrieved results.

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