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A Fast Similarity Algorithm for Personal Ontologies Using Triangle Inequality
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|摘要:||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。|
The 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.
|Appears in Collections:||資訊科學與工程學系所|
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