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 5351 次，被下載 2386 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.

[AL99] Y. Aumann and Y. Lindell, "A statistical theory for quantitative association rules," Proc. of the 5'th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining, pp. 261-270, 1999.

[AS93] R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington DC, pp. 207-216, 1993.

[AS94] R. Agrawal and R. Srikant, "Fast algorithm for mining association rules," Proc. of the 20th VLDB Conf., pp. 478-499, Sept. 1994.

[ASY98] C. C. Aggarwal, Z. Sun, and P. S. Yu, "Online algorithms for finding profile association rules," Proc. of 5th Int'l. Conf. on Information and Knowledge Management (CIKM98), pp. 86-95, 1998.

[Bala97] M. Balabanovic, "An adaptive web page recommendation service," Proc. of the 1'st Int'l. Conf. on Autonomous Agents, Marina del Rey, CA, 1997.

[BHK98] J. S. Breese, D. Heckerman and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Tech. Report, MSR-TR-98-12, Microsoft Research, Oct. 1998.

[BMS97] S. Brin, R. Motwani and C. Silverstein, "Beyond market baskets: generalized association rules to correlations," Proc. of the ACM SIGMOD Conf. on Management of Data, pp. 265-276, 1997.

[CACM92] Special issue on information filtering, Communications of the ACM, 35(12), Dec. 1992.

[HC80] R. V. Hogg and A. T. Craig, Introduction to Mathematical Statistics, 4'th ed., MacMillan Publishing, New York, 1980.

[HCC93] J. Han, Y. Cai, and N. Cercone, "Data-driven discovery of quantitative rules in relational databases," IEEE Trans. Knowledge and Data Engineering, Vol. 5, pp. 29-40, 1993.

[HF95] J. Han and Y. Fu, "Discovery of multiple-level association rules from large databases," Proc. of the 21st VLDB Conf., pp. 420-431, 1995.

[Hong01] C.-Y. Hong, The Research on Finding Generalized Association Rules from Library Circulation Records, master thesis, National Sun Yat-sen University, 2001.

[HL02] S.-Y. Hwang, E.-P. Lim, 2002, "A Data Mining Approach to Library New Book Recommendations," Proc. of the Int'l. Conference on Asia Digital Libraries (ICADL2002), Singapore. LNCS 2555, Springer Verlag.

[HT97] R. V. Hogg and E. A. Tanis, Probability and Statistical Inference, 5'th ed., Prentice-Hall Inc., 1997.

[JS02] S. Jaroszewicz and D. A. Simovici, "Pruning redundant association rules using maximum entropy principle," Proc. of 6'th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2002), Taipei, Taiwan, 2002.

[KB96] B. Krulwich and C. Burkey, "Learning user information interests through extraction of semantically significant phrases," Proc. of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA, 1996.

[Kim01] J. W. Kim, B. H. Lee, M. J. Shaw, H. L. Chang, and M. Nelson, "Application of Decision-tree Induction Techniques to Personalized Advertisements on Internet Storefronts," International Journal of Electronic Commerce, 5(3), pp. 45-62, 2001.

[LAR00] W. Lin, S. A. Alvarez, and C. Rujz, "Collaborative recommendation via adaptive association rule mining," Proc. Of WebKDD Workshop, 2000.

[LHM99] B. Liu, W. Hsu, and Y. Ma, "Pruning and summarizing the discovered associations," Proc. of the 5'th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining, pp.125-134, N.Y. Aug., 1999.

[MR00] R. J. Mooney and L. Roy, "Content-based book recommending using learning for text categorization," Proc. Of the 5'th ACM Conf. on Digital Libraries, pp. 195-240, June 2000.

[Pazz99] M. J. Pazzani, "A framework for collaborative, content-based and demographic filtering," Artificial Intelligence Review, pp. 393-408, 1999.

[PHLG00] D. Pennock, E. Horvitz, S. Lawrence and C. L. Giles, "Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach," Proc. of the 6'th Conf. on Uncertainty in Artificial Intelligence (UAI-2000), pp. 473-480, 2000.

[PT00] B. Padmanabhan and A. Tuzhilin, "Small is beautiful: discovering the minimal set of unexpected patterns," Proc. of the 6'th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining, pp.54-63, Aug. 2000.

[RP97] J. Rucker and M. J. Polanco, "Siteseer: personalized navigation for the web," Communications of the ACM, 35(12), pp. 73-75, Dec. 1992.

[SA95] R. Srikant and R. Agrawal, "Mining generalized association rules," Proc. of the 21st VLDB Conf., pp. 409-419, 1995.

[SA96] R. Srikant and R. Agrawal, "Mining quantitative association rules in large tables," Proc. of the ACM SIGMOD Conf. on Management of Data, pp. 1-12, June 1996.

[SK98] E. Suzuki and Y. Kodratoff, "Discovery of surprising exception rules based on intensity of implication," Proc. of PKDD-98, France, p.10-18, 1998.

[SKKJ01] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," Proc. Of the 2nd ACM Conference on World Wide Web, Hong Kong, pp. 285-295, 2001.

[SKR01] J. B. Schafer, J. A. Konstan, and J. Riedl, "E-Commerce Recommendation Applications," Data Mining and Knowledge Discovery, 5(1), pp. 115-153, 2001.

[SM95] U. Shardanand and P. Maes, "Social Information Filtering: Algorithms for Automating 'Word of Mouth'," Proc. Of the Conference on Human Factors in Computing Systems (CHI'95), pp. 210-217, 1995.口試委員魏志平 - 召集委員

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