臺灣博碩士論文加值系統

現代圖書館的館藏與日遽增，而同一主題的書也有相當多的版本，甚至有些館藏屬於電子書，讀者要找到自己有興趣的主題，同時又具有代表性的書籍並不容易，往往在輸入一關鍵字之後，查詢系統所提供的書籍相當多，讀者需要花很多時間瀏覽才能找到具代表性的書籍。其次，由於現代資訊發達，書籍琳瑯滿目，要如何找尋適合自己閱讀的書籍也是很多讀者苦苦尋找的方法。本研究以圖書館的借閱紀錄為基礎，運用資料探勘(Data Mining)技術來找出圖書館讀者的閱讀興趣，從這些探勘的結果中，可以探究讀者和讀者之間閱讀的相似性，接著了解書籍和書籍之間的關聯性，依照讀者的閱讀興趣推薦給擁有相似閱讀興趣讀者適合的館藏書籍。本研究主要利用關聯規則Apriori方法幫讀者找出和他擁有相似閱讀興趣的讀者，因為閱讀的興趣類似，所以推薦彼此借閱的書籍給同一族群的讀者有較高的參考價值。其次，瞭解存在關聯性的書籍之推薦度，將具有高推薦度之書籍推薦給同一族群的讀者，最後，對映到讀者的個人興趣產生推薦清單。本研究以玄奘大學的借閱記錄為研究對象，透過本研究希望能有效的利用圖書館的系統資源，提高圖書館系統的價值。

The storage of library is increasing and the books with the same theme have quite a lot of editions, including some e-books. Therefore, it is difficult for readers to find their own interesting theme and the representative booklists. Secondly, when one keyword is provided by readers, quite a lot of booklists are offered so that the readers must spend much time browsing through to find the representative booklists. Furthermore, because of current much and diverse information, the readers is suffering from looking for the suitable booklists for themselves. Based on borrowing records of the library system, this study tries to apply the data mining techniques to find out the interest of reading for the library readers. From the result excavated, the similarity of readers’ interest for reading and the association of the books will be obtained. By the similar reading interest, the booklists will be recommended. In our proposed approach, an association rule, Apriori algorithm, is first applied to find out the groups with the same reading interest. Secondly, the recommendation degree of the associated booklists will be computed and the booklists will be recommended to the same reading group by the higher degree. An experiment data in HCU (Hsuan Chuang University) library shows the improvement in effectiveness of the use of library resources.

第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 5
第一節 資料探勘 5
第二節 個人化服務 15
第三節 推薦系統介紹 20
第四節 國內外圖書館系統個人化之背景 24
第五節 國內關於圖書館資訊個人化系統相關研究 27
第三章 研究方法 29
第一節 資料的前置處理 31
第二節 建立讀者興趣資料庫 33
第三節 讀者興趣分群 36
第四節 探勘推薦書籍之關聯法則 39
第五節 回饋 44
第六節 推薦系統的流程步驟 46
第四章 系統實作 49
第一節 系統環境 49
第二節 前置資料處理 51
第三節 推薦系統建置流程 55
第四節 推薦系統精確度分析 63
第五章 結論及未來工作 67
第一節 結論 67
第二節 研究限制 68
第三節 未來研究方向 69
參考文獻 71

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