臺灣博碩士論文加值系統

對圖書館而言，降低資訊負荷對使用者進行資訊檢索時的苦難與提升冷門館藏的曝光度及利用率，一直是一個重要的挑戰。推薦系統是解決這些挑戰最好的工具之一，然而推薦系統卻必須面對侵犯使用者隱私與資料稀缺等問題。本研究以結合具有龐大資料量及真實消費者行為之網路書店推薦資料與圖書館館藏資料的方式當作推薦系統的資料來源，用以解決上述所遭遇的問題。借鑑協力式過濾之演算法，推演出適用於本研究之推薦度（R）公式，用以計算非透過使用者之間相似度為主的推薦方法。透過實證結果發現，透過結合網路書店資料一定程度上降低了隱私與資料稀缺問題，並提升冷門館藏的曝光度。

Information overload and enhance the usage of low borrow library collections is the challenges of Library. Recommender Systems (RSs) is one of the best tool to overcome these challenges, but its’ always come along with the issues like privacy and data sparsity. This research aim to overcome the challenges and issues above with integrating consumer behavior from online bookstore. Proposed algorithm is Reference by Collaborative Filtering to infer the R-index for the target and the recommend books. The results show that by integrating data from online bookstore can reduce privacy and sparsity issues for recommender system and enhance the visibility of the low borrow collections.

中文摘要 I
英文摘要 II
誌 謝 III
圖目錄 VI
表目錄 VII
公式目錄 VIII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究內容與方法 2
1.2.1 資料來源 2
1.2.2 推薦取向 3
1.3 研究範圍與限制 3
1.4 研究流程 4
1.5 論文大綱 6
第二章 文獻回顧 7
2.1 圖書館與資訊檢索 7
2.2 推薦系統 8
2.2.1 推薦系統簡介 8
2.2.2 資料來源 11
2.2.3 推薦取向 14
2.2.3.1 協力式過濾 14
2.2.3.2 內容導向過濾 17
2.2.3.3 常見問題 18
2.2.3.4 混合過濾 19
第三章 系統設計與架構 21
3.1 系統架構 21
3.2 資料來源 22
3.2.1 原始資料與選擇準則 22
3.2.1.1 資料來源 22
3.2.1.2 資料來源選擇準則 23
3.3 前置處理模組 24
3.3.1 館藏資料與借閱紀錄 25
3.3.2 網路書店資料 27
3.4 資料接收模組 27
3.5 資料處理模組 28
3.5.1 館藏資料 29
3.5.2 網路書店資料 31
3.6 邏輯判斷模組 31
3.6.1 借閱紀錄 32
3.6.2 網路書店資料 36
3.6.3 整合清單 37
3.7 資料呈現模組 38
第四章 實證發現與結果分析 40
4.1 資料來源的發現與增益 40
4.1.1 資料可用性 40
4.1.2 資料稀缺與隱私 42
4.2 使用者與系統互動流程 43
4.3 館藏推薦增益 44
4.3.1 冷門館藏曝光 45
4.3.2 權重組合與推薦結果年代 46
4.3.3 權重組合與推薦結果借閱次數 48
4.3.4 推薦目的與結果 50
4.4 其他應用 51
第五章 結論與未來研究 52
5.1 研究結果與貢獻 52
5.2 未來工作 53
參考文獻 54
附錄 58

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