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研究生:張毓倫
研究生(外文):Yu-Lun Chang
論文名稱:個人化顯隱性知識推薦方法之研究
指導教授:王惠嘉王惠嘉引用關係
指導教授(外文):Hei-Chia Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:83
中文關鍵詞:協同過濾電子化文件內容過濾個人化推薦系統
外文關鍵詞:Recommendation System.PersonalizationContent Filteringe-documentsCollaborative Filtering
相關次數:
  • 被引用被引用:21
  • 點閱點閱:336
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
網際網路(Internet)與全球資訊網(World Wide Web;WWW)技術的蓬勃發展,使得各式各樣的資訊都能經由網路取得,此外,數位電子化技術的成熟也促使電子圖書、e-journal等網際網路資源快速增加。藉由網際網路與數位資訊的推波助瀾,使用者可以不受時空限制地透過瀏覽器查詢數位化後的各類型文件資源。網際網路所提供的豐富資訊雖然讓使用者擁有更多的選擇機會,卻也同時產生「資訊過載」(information overload)的困擾。這使得從網際網路中找到真正相關的資訊變成一件費時且沒有效率的工作。目前電子化文件的查詢方法一般為透過搜尋引擎的關鍵字查詢,搜尋引擎的關鍵字搜尋結果卻面臨三個問題:第一、搜尋的結果常含有過多未精練的資訊,使用者必須一一過濾以取得符合需要的資訊;第二、使用者必須先很明確的知道想要搜尋資料的方向及關鍵字,才能有效的利用搜尋功能;第三、搜尋的方式在於只要搜尋字串及條件符合,即會將結果提供給使用者,因此不一定能符合使用者的需求與興趣;此外,在強調知識管理的今天,我們對於知識的取得,卻往往只強調顯性的知識,而忽略了隱性知識最重要來源「人」的溝通價值。
爲了提供更有效益的顯隱性知識以及改善資訊與知識搜尋結果未適性化的問題,本研究提出一個人化顯隱性知識推薦方法來提供個人化資訊推薦服務,以滿足每位使用者對於顯性知識文件與隱性知識同好的需求。其推薦方法的設計主要是結合協同過濾與內容過濾兩者,以協同過濾考慮群組興趣的概念來過濾文件品質並實施隱性知識的推薦;再以內容過濾來緩和協同過濾評比稀疏及cold start的問題,兩種資訊過濾方式併行,避免單一方式所衍生的問題。同時本研究實際建置一「個人化推薦模型」(Personalized Recommendation Model;PRM),以E-journal paper推薦為範例進行一實驗研究,並以不同的評估指標來分析與評估PRM的推薦績效,結果顯示不論在MAE值、precision值、recall值與F-measure值的表現上,都有明顯提昇的推薦效果,足以驗證本研究所提出的個人化推薦方法之效果。
As the rapid development of Internet and World Wide Web (WWW), the Internet and WWW have become a major information and knowledge source for people. The progress of the electronic technology has also caused the style of conventional paper-publishing documents changed and toward the electronic style. By the Internet, people could use browsers to search various digitalized documents without spatial and temporal constraints. The Internet provides a powerful disseminative ability for users to acquire information more efficient and fast and provides opportunities for information findings. However, the exponentially increasing information provided by the Internet causes the problem of information overload and the rapid growth of the number of e-documents has also made information discovery from the Internet a time-consuming and inefficient task.
In general, we search proper e-documents through search engine service. Search engine allows user to retrieve relevant papers by entering keywords. Although a search engine provides convenient documents search, there are still three serious problems that affect search engine service: First, users have to check many returned papers in order to locate the desired information. Second, users must know exactly what keywords will lead them to the right direction. Third, we usually focus on the recommendation of explicit knowledge, but ignore the importance of tacit knowledge recommendation. And those problems become worse along with the information overload.
To overcome the problems mentioned above, we propose a personalized e-documents recommendation method to provide personalized information service. Our method combines Content Filtering and Collaborative Filtering and consists of two parts: tacit knowledge recommendation and explicit knowledge recommendation. This method can automatically extracts the interests of a user according to his past ratings and actions. After we find out the interests of a user, we can recommend information that is related to the interests of the user. Finally, we implement a Personalized Recommendation Model (PRM) and use MAE, precision, recall and F-measure to evaluate the performance of PRM. And the results show that PRM performs well in those measures. PRM can not only create the specialty, but also improve the satisfaction of e-documents information service
中文摘要Ⅰ
英文摘要Ⅱ
誌謝Ⅲ
目錄Ⅳ
圖目錄Ⅵ
表目錄Ⅶ
第一章、緒論1
第一節、研究背景與動機1
第二節、研究目的4
第三節、研究流程5
第四節、研究範圍與限制6
第五節、論文架構7
第二章、文獻探討8
第一節、網際網路興盛與資訊過載問題8
2.1.1 概述8
2.1.2 搜尋引擎與目錄服務系統9
第二節、個人化服務9
2.2.1 個人化服務的意義10
2.2.2 個人化服務與客製化服務11
2.2.3 個人化的層次12
2.2.4 個人化服務的項目13
2.2.5 個人化的技術14
第三節、推薦系統15
2.3.1 推薦系統介紹15
2.3.2 推薦系統評分方式15
2.3.3 推薦技術的分類與user profiles的學習方法18
2.3.4 協同過濾的挑戰與相似度計算方法23
2.3.5 推薦系統相關研究26
第三章、研究方法31
第一節、個人化推薦模型的架構31
第二節、採用內容過濾與協同過濾兩者結合的原因32
3.2.1 採用協同內容式過濾的原因32
3.2.2 協同內容式推薦相關比較33
第三節、個人化顯性知識之推薦設計38
3.3.1 資料收集38
3.3.2 Paper關鍵字結構分析39
3.3.3 使用者評分值取得與關鍵字興趣表建立40
3.3.4 協同內容式過濾44
3.3.5 CB +CF演算法51
第四節、個人化隱性知識之推薦設計53
第四章、實作驗證55
第一節、實作發展55
4.1.1 PRM實作環境55
4.1.2 資料庫設計55
4.1.3 PRM模型的Use Case Diagram56
第二節、實驗方法與比較項目60
4.2.1 資料描述60
4.2.2 實驗設計原則62
4.2.3 評估指標62
4.2.4 實驗比較項目64
第三節、實驗結果與分析65
第四節、討論71
第五章、結論與未來研究方向73
第一節、研究結果與貢獻73
第二節、未來可參考的研究方向76
參考文獻77
圖 1-1 研究流程圖5
圖 2-1 評分值的分類17
圖 3-1 個人化推薦模型架構圖31
圖 3-2 Balabanovic & Shoham(1999)之CB+CF結合設計35
圖 3-3 Claypool et al. (1999) 之CB+CF結合設計35
圖 3-4 Good et al. (1999)之CB+CF結合設計35
圖 3-5 Bollacker et al. (2000)之CB+CF結合設計36
圖 3-6 本研究PRM之CB+CF結合設計36
圖 3-7 內容過濾與協同過濾於顯性知識推薦之流程38
圖 3-8 明顯性評分與隱含性評分結合方法41
圖 3-9 協同內容式推薦過程44
圖 3-10 paper 評分值的相似度計算概念46
圖 3-11 關鍵字興趣值的相似度計算概念47
圖 3-12 預測值求算概念49
圖 3-13 內容過濾與協同過濾於隱性知識推薦之流程53
圖 4-1 PRM之Use Case Diagram56
圖 4-2 搜尋結果介面57
圖 4-3 搜尋結果與評分介面57
圖 4-4 paper推薦介面58
圖 4-5 paper推薦與回饋介面59
圖 4-6 同好推薦介面60
圖 4-7 協同內容式過濾之MAE值65
圖 4-8 協同內容式過濾之precision值與recall值66
圖 4-9 協同內容式推薦之F-measure值67
圖 4-10 考慮不同近鄰數目之MAE值68
圖 4-11 考慮不同近鄰數目之precision值 (threshold為3)69
圖 4-12 考慮不同近鄰數目之recall值(threshold為3) 70
圖 4-13 考慮不同近鄰數目之F-measure值(threshold為3) 71
表 2-1 明顯性評分與隱含性評分的優缺點比較18
表 2-2 User1對5篇文件的評分20
表 2-3 5個使用者對5篇文件的評分21
表 2-4 5個使用者對Paper1的評分21
表 2-5 5個使用者的CB profile以及對Paper1的評分值23
表 3-1 協同內容式推薦相關研究 34
表 3-2 本研究與先前研究歸納比較37
表 3-3 Paper關鍵字結構表40
表 3-4 從使用者的動作來決定paper的評分值42
表 3-5 使用者 關鍵字興趣表43
表 4-1 基本使用者資料61
表 4-2 推薦評估方式整理63
表 4-3 評分實際值與預測值的矩陣64
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