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研究生:蔡宗哲
研究生(外文):Tsung-Che Tsai
論文名稱:網際網路中觀念搜尋之研究
論文名稱(外文):Concept-based Web Search by Interactive Evolutionary Algorithms
指導教授:李偉柏
指導教授(外文):Wei-Po Lee
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系
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:2002
畢業學年度:90
語文別:中文
論文頁數:57
中文關鍵詞:演化策略遺傳演算法搜尋關鍵字中心向量互動性
外文關鍵詞:keyword-basedEvolution Strategies Algorithmquality of Web search
相關次數:
  • 被引用被引用:0
  • 點閱點閱:395
  • 評分評分:
  • 下載下載:131
  • 收藏至我的研究室書目清單書目收藏:3
搜尋引擎與入口網站是一種普遍獲得資訊的工具,但在應用上所產生的搜尋問題如:資訊太少、資料過多、資訊方向偏離…等,造成使用者未能得到滿意的資訊,或者需要耗費過多的時間過濾不必要的資料。因此本研究針對搜尋上的問題,探討一套新的搜尋觀念,以網頁推薦的方式取代一般使用單一關鍵字搜尋的方法。本研究以使用者點選網頁的互動方式,蒐集網頁內容的關鍵字組成為一組中心向量,再利用演化策略(Evolutionary Strategies)遺傳演算法針對中心向量進行演化程序,進而推薦最佳的關鍵字組。利用產生出之關鍵字組搜尋資訊,並過濾網頁中不必要資料以不相關的網頁,進而推薦符合的使用者個人化搜尋資訊,在低互動性中得到使用者滿意資訊,本研究所進行的實驗與結果探討將會驗證ES對於搜尋品質的提昇。

Search engines are useful tools in looking for information from the Internet. However, due to the methods of keyword-based similarity ranking used in the conventional search engines, people may still suffer from spending a large amount of time and effort to examine and browse the results presented by a search engine but not find the specific information he really wants. To remedy the above problem, in this thesis we develop a new search mechanism that can remove unrelated pages from Web search results. Through interacting with the users, the system can recommend them Web pages related their interests and extract critical features from the pages a user has selected. In addition, the Evolution Strategies Algorithm is employed to evolve the features to best describe the concept in a user’s mind. Therefore, the quality of Web search can be largely improved. By repeating the above procedure, new queries can be formulated for the search engines to retrieve more pages to match a user’s interests. Our experimental results show that the Evolution Strategies Algorithm is efficient and useful to enhance the quality of Web search.

目 錄
摘要 I
誌謝 Ⅲ
圖目錄 Ⅵ
表目錄 Ⅶ
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究流程 4
第二章、文獻探討 5
2.1 使用者興趣資訊 5
2.2 搜尋機制 9
2.3 過濾與學習機制 11
第三章 研究架構 15
3.1 研究機制架構與元件介紹 17
3.2 實驗流程 19
3.2.1互動式搜尋 21
3.2.2 Evolutionary Strategies 24
第四章 實驗結果評估與討論 28
4.1傳統的搜尋方式 30
4.2 互動式搜尋 32
4.3分段式互動遺傳演算法搜尋 36
4.4互動式遺傳演算法搜尋 41
第五章 結論與未來研究 49
5.1 結論 49
5.2 未來研究 51
參考文獻 52
作者簡介 57
圖 目 錄
圖2-1 Sykill&Webert 6
圖2-2 Letizia 7
圖3-1 研究架構圖 16
圖3-2 使用者介面圖 17
圖3-3 向量餘弦公式圖 23
圖3-4中心向量圖 24
圖3-5 ES演化流程圖 26
圖4-1 computer science分段式互動遺傳演算法搜尋比較圖 37
圖4-2 ai分段式互動遺傳演算法搜尋比較圖 38
圖4-3 intelligent agent分段式互動遺傳演算法搜尋比較圖 39
圖4-4 computer science互動式遺傳演算法搜尋比較圖 42
圖4-5 computer science互動式遺傳演算法累積搜尋比較圖 42
圖4-6 ai互動式遺傳演算法搜尋比較圖 44
圖4-7 ai互動式遺傳演算法累積搜尋比較圖 44
圖4-8 intelligent agent互動式遺傳演算法搜尋比較圖 46
圖4-9 intelligent agent互動式遺傳演算法累積搜尋比較圖 46
表 目 錄
表2-1學習機制比較表 14
表4-1傳統搜尋結果表 28
表4-2 computer science互動式搜尋比較表 30
表4-3 ai互動式搜尋比較表 31
表4-4 intelligent agent互動式搜尋比較表 31
表4-5 computer science分段式互動遺傳演算法搜尋比較表 37
表4-6 ai分段式互動遺傳演算法搜尋比較表 38
表4-7 intelligent agent分段式互動遺傳演算法搜尋比較表 39
表4-8 computer science互動式遺傳演算法搜尋比較表 41
表4-9 ai互動式遺傳搜尋比較表 43
表4-10 intelligent agent互動式遺傳搜尋比較表 45

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