臺灣博碩士論文加值系統

近年來網際網路的蓬勃發展，人們學習新知識的方式不再侷限於書本上，隨著各大主流的搜尋引擎崛起，已成為現代人們分享、學習知識的一項重要管道。然而網路資訊日新月益，資料量愈趨龐大，但目前各大主流的搜尋引擎卻是單使用者的設計模式，導致無法提供人們進行協同合作過濾龐大的資訊。
本研究之主要目的是藉由基於雲端運算所開發的社會性書籤(Social Bookmark)網站─MyBook及IE Toolbar，協助人們進行協同合作與網站篩選，進而分享、學習知識。本研究利用使用者的書籤資訊，計算使用者彼此間的興趣相似程度，作為網站推薦機制及交友機制，並結合Google PageRank與聲譽模式(Reputation-based Model)，排序其網站與好友的推薦結果。因此本研究的主要貢獻為提供基於雲端運算所開發的協同合作平台，並結合現有的搜尋引擎，達到社群交友、協同合作及知識分享，除了能結識興趣相同的其他使用者之外，還能透過他人的經驗，在短時間內獲得所需之資訊。
本研究最後透過為期一星期的系統實測，蒐集52位使用者的行為資訊，並從中探討相似度、聲譽值、PageRank與引用次數彼此間之關係、使用者角色及社會性書籤系統設計等相關議題。

The development of Internet is thriving in recent years. People don't confined learn the new knowledge to books. After search engines appear, people can easily learn and search the new knowledge. Unfortunately amount of data is getting huge in the internet, many search engines do not explicitly support collaboration during search and this let people can not collaborate to filter data.
This paper introduces two systems that social bookmarking website based on cloud computing and IE Toolbar. These systems support users by harnessing the bookmark's metadata、users' similarity calculation、PageRank and Reputation Model as the base for websites and users recommendation. Therefore, a key contribution of this paper is to develop a collaboration system and to design IE Toolbar to work with mainstream search engines. Let user can easily make friend with another user, collaborate to filter data and share their knowledge.
Finally, the experiment invites 52 users and they use this system about one week. The research findings show that reputation, PageRank and citation times are positive relationship. Personality and interactivity can enrich the content and enlarge the user interesting in this system.

中文摘要 i
英文摘要 ii
目 錄 iii
表 目 錄 vi
圖 目 錄 vii
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究流程 2
1.4 名詞定義 3
第2章 文獻探討 4
2.1 社會性書籤網站(Social Bookmarking Website) 4
2.1.1 書籤(Bookmark) 4
2.1.2 社會性書籤網站之定義 4
2.1.3 社會性書籤網站之品質 5
2.2 網頁搜尋(Web Search)相關研究 6
2.2.1 相似度排序法 6
2.2.2 靜態排序法 7
2.3 雲端運算 8
2.3.1 雲端運算簡介 8
2.3.2 雲端基礎設備服務(Infrastructure as a Service) 9
2.3.3 雲端平台服務(Platform as a Service) 9
2.3.4 雲端應用服務(Software as a Service) 9
第3章 系統架構 10
3.1 系統架構 10
3.2 系統流程說明 11
3.3 推薦機制 12
3.3.1 好友推薦機制 12
第4章 系統實作與說明 19
4.1 系統實作環境與開發工具 19
4.2 SQL Azure資料庫設計 19
4.3 BLOB 21
4.4 系統實作畫面 22
4.3.1 IE Toolbar 22
4.3.2 MyBook 26
4.3.3 社交圈建立 35
第5章 實驗結果 39
5.1 資料來源 39
5.2 實驗結果 40
5.2.1 聲譽值、PageRank與引用次數 40
5.2.2 生產者與消費者 41
5.2.3 相似度與聲譽值 42
5.2.4 系統優點與缺失 42
第6章 結論與未來展望 50
6.1 結論 50
6.2 未來展望 51
參考文獻 52

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