姓名賴雨廷(Yu-Ting Lai) 電子郵件信箱m9042638@student.nsysu.edu.tw 畢業系所資訊管理學系研究所(Information Management) 畢業學位碩士(Master) 畢業時期91學年第2學期 論文名稱(中)利用資料探勘技術應用於圖書館新書推薦之研究 論文名稱(英)A Data Mining Approach to New Library-Book Recommendations 檔案本電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。

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電子論文：校內外都一年後公開論文語文/頁數英文/40 統計本論文已被瀏覽 5357 次，被下載 2400 次 摘要(中)在這篇論文當中，我們利用資料探勘的方式應用到圖書館中仍未被評鑑或是被讀者借閱過的新書推薦。在研究當中，使用者會根據他們的屬性資料而給予特性化，階層化的觀念也會被定義在這些屬性之中。再者，書本也會根據其種類而給予不同的分類。首先，我們要先辨識出什麼類型的讀者喜歡哪一種類的書籍，這部分的資訊我們稱其為正規化關聯式規則。興趣性較低或是多餘的規則將會刪除，以形成有效規則集合，而這些集合就是我們之後要作為推薦新書用的。針對新書推薦的規則，我們也定義了一種新的逼近方式來計算其感興趣的程度，以及能有效地建立推薦的計劃。最終我們也實際利用圖書館的借閱紀錄來實驗，來評估這些計劃所能呈現的校能。 摘要(英)In this thesis, we propose a data mining approach to recommending library new books that have never been rated or borrowed by users. In our problem context, users are characterized by their demographic attributes, and concept hierarchies can be defined for some of these demographic attributes. Books are assigned to the base categories of taxonomy. The proposed approach starts with the identification of the type of users who are interested in some specific type of books. We call such knowledge generalized profile association rules. Less interesting or redundant generalized profile association rules are then pruned to form a concise rule set. The resultant rule set is then used for promotion of new books. We develop a new definition of rule interestingness with respect to book recommendation, propose an approximation scheme for estimating the interestingness of a rule, and construct a scheme to effectively conduct new book recommendation by using the interesting rules. We finally apply the book circulation data of a university library to the proposed approach for performance evaluation. 關鍵字(中)階層化的觀念 正規化關聯式規則 資料探勘 關鍵字(英)data mining generalized profile association rules concept hierarchies 論文目次Chapter 1 Introduction.......1

Chapter 2 The Problem.......5

Chapter 3 Identifying Generalized Profile Association Rules.......7

Chapter 4 Defining rule interestingness.......9

Chapter 5 Selecting new library books for recommendation.......19

Chapter 6 Evaluations.......26

6.1 Description of the experimental data.......26

6.2 Experimental results.......27

- Accuracy of estimated interestingness.......27

- Effectiveness of book selection strategy.......30

- Prediction power of the proposed method.......33

Chapter 7 Conclusions.......36參考文獻[AEK00] A. Ansari, S. Essengaier, and R. Kohli, "Internet Recommendation Systems," Journal of Marketing Research, 37(3), Aug. 2000.

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簡立峰 - 委員

黃三益 - 指導教授

口試日期2003-07-10 繳交日期2003-08-11