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dc.contributorChaur-Chin Chenen_US
dc.contributorCheng-Yan Kaoen_US
dc.contributorShang-Juh Kaoen_US
dc.contributor.advisorI-En Liaoen_US
dc.contributor.authorWang, Huan-Yuen_US
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dc.description.abstract本論文以中興大學圖書館的館藏與讀者相關資料為基礎,延續本實驗室已開發的圖書館推薦系統(PORE: Personal Ontology Recommender System),著重在改善個人本體論相似度計算時間的研究。PORE系統可區分為以內容為基礎(content-based)的個人本體論模型推薦模式,及以結合合作式過濾(collaborative filtering)為基礎的推薦模式。由於合作式過濾需要比對所有使用者之間的相似度,而中興大學圖書館大約有3萬位使用者有借閱紀錄,及30多萬中英文館藏,其兩兩比對的計算,往往需要非常久的時間。 本論文在已開發的個人本體論合作式過濾推薦系統上,設計並實作了一個改善個人本體論相似度計算時間的演算法。因為個人本體論可看成一個樹狀的結構,所以此演算法採用樹距離(tree distance)的概念,重新定義了使用者的相似度,並運用了三角不等式(triangle inequality)的原理,省略了部份明顯不相似的使用者之間的相似度計算。從實驗結果得知,本研究的方法相較於暴力破解法,在計算分群後的本體論相似度之效能上有顯著的改善,且約可節省88%的使用者相似度比對的運算。zh_TW
dc.description.abstractThe Personal Ontology Recommender System (PORE) currently operated in the library of National Chung Hsing University is a recommender system developed by our research team. The system consists of content-based recommendation model based on personal ontology and collaborative filtering recommendation model. For collaborative filtering, the recommender system needs to compute the similarity between any two users. That will incur lots of computations because the library currently has more than thirty thousands of users and three hundred thousands of collections. The purpose of this thesis is to design an efficient algorithm for computing the similarity between two users. A personal ontology representing the favorites of a user in PORE is a tree structure. In this thesis, we define tree distance for measuring the dissimilarity between two users. We then propose an efficient algorithm for calculating ontology similarities using triangle inequality. The experimental results show that the proposed method can save up to 88% of comparisons compared to that of brute force algorithm.en_US
dc.description.tableofcontents摘要 i Abstract ii 第一章 緒論 1 1.1研究背景與動機 1 1.2問題描述 2 1.3論文主要貢獻 2 1.4論文架構 3 第二章 相關研究 4 2.1中國圖書分類法(Classification Scheme for Chinese Libraries) 4 2.2本體論(Ontology) 7 2.3個人本體論圖書館推薦系統 11 2.4合作式過濾技術(Collaborative Filtering Technology) 13 第三章 PORE系統簡介 19 3.1圖書館讀者的個人本體論 19 3.2描述個人本體論的個別節點之內容 21 第四章 個人本體論相似度函數 22 4.1研究概念及其流程與週期 22 4.2以樹距離(tree distance)定義相似度函數 24 4.3計算本體論相似度演算法 26 4.4演算法設計 29 4.4.1暴力破解法 29 4.4.2 TDTI演算法 30 第五章 系統實作及實驗結果探討 33 5.1資料集及資料表 33 5.2 系統環境及架構 35 5.3 實驗設計及相關參數設定 37 5.3.1 Localhost架構 37 5.4 實驗結果與分析探討 38 5.4.1 實驗一:暴力破解法與TDTI執行效率之比較 38 5.4.2 實驗二:不同β值的執行效率之比較 41 5.4.3 實驗三:不同β值可省略計算之比較 42 5.4.4 實驗四:分群後,暴力破解法與TDTI執行效率之比較 44 第六章 結論與未來研究 46 6.1 結論 46 6.2 未來研究 47 參考文獻 48zh_TW
dc.subjectPersonal ontologyen_US
dc.subjectCollaborative filteringen_US
dc.subjectTree distanceen_US
dc.subjectTriangle inequalityen_US
dc.titleA Fast Similarity Algorithm for Personal Ontologies Using Triangle Inequalityen_US
dc.typeThesis and Dissertationzh_TW
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