隨著網際網路(World Wide Web)的蓬勃發展，在它上面分享的資訊量幾乎是每幾個月就成長一倍，早期資訊不足的問題在現今網際網路的時代已經不會再發生，取而代之的是資訊過量的問題。有鑒於此，本研究採用傳統分析內容(Content-based)方法分析網頁內容相似度，結合PageRank中Random Surfer之概念，建立網頁間連結架構以呈現網頁間連結關係，希望藉由引入網頁連結架構的分析，為使用者找出重要且相關的網頁，來判別出高品質(High-Quality)且具權威性(Authoritiveness)的網頁來進行推薦。利用本研究提出的兩種方法與傳統分析網頁內容進行推薦的方式，針對使用者瀏覽記錄評估推薦效益，實驗結果發現本研究所提出的兩種方法能得到較佳的推薦效果。

As the rapid growth of WWW websites, the amount of information in the Web has spawned on an unpredictable scale. We cannot effectively digest the information that we can get from the Web. In this paper, we therefore propose a novel approach that utilizes the concepts of classic content-based anlaysis, and combines PageRank for making web recommendation. Specifically, the proposed approach analyzes the link structure of the web pages. We try to identify that web pages has high-quality and authoritiveness for users. The experiment results show that our approach yields effective recommendation.

誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖次 v
表次 vi
第一章 緒論 1
第二章 文獻探討 4
第一節 推薦系統 4
一、分析內容為基礎的網頁推薦方式 5
二、協同式為基礎的網頁推薦方式 10
三、網頁使用探勘 13
第二節 網頁在網際網路中的權威性 15
一、The InDegree Algorithm 17
二、The PageRank Algorithm 17
三、The HITS Algorithm 19
四、The SALSA Algorithm 20
第三節 網頁使用日誌 22
第三章 以連結關係為基礎的網頁推薦方法 24
第四章 實驗結果與討論 31
第一節 研究資料蒐集與處理 31
一、日誌(Logs)內容 31
二、網頁內容 36
第二節 實驗設計說明 37
一、精確度(Precision) 38
二、涵蓋率(Recall) 39
三、F1指標 39
第三節 實驗結果與討論 40
一、精確度(Precision) 40
二、涵蓋率(Recall) 42
三、F1指標 43
第五章 總結及未來研究 45
第一節 結論 45
第二節 研究限制 45
第三節 未來研究方向 46
參考文獻 47

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