利用興趣加權探勘技術發掘書籍借閱之適性化推薦

Using Mining Techniques with Weighted Interest-Items to Find Adaptive Recommendations of Borrowing Books

10.7060/KNUJST.200712.0053

陳垂呈(Chui-Cheng Chen)

資料探勘 ； 興趣加權關聯規則 ； 借閱資料 ； 書籍借閱 ； 適性化推薦 ； data mining ； association rules with weighted interest-items ； borrowing history records ； borrowing books ； adaptive recommendations

高雄師大學報：自然科學與科技類

23期（2007 / 12 / 01）

53 - 76

繁體中文

本論文以讀者之借閱資料為探勘資料來源，每一筆借閱資料包含讀者曾經借閱的書籍，利用興趣加權探勘技術分別從以下兩方面發掘書籍借閱的適性化推薦：一是以某一讀者為探勘的目標，本研究提出一個探勘興趣加權關聯規則(association rules with weighted interest-items)的演算法，興趣加權關聯規則之前置項目組必須包含於此讀者的借閱資料中，根據此興趣加權關聯規則所顯示出的傾向特徵，可發掘此讀者之書籍借閱的適性化推薦；二是以某一書籍為探勘目標，本研究提出一個探勘興趣加權關聯規則的演算法，興趣加權關聯規則之後置項目組必須為此書籍，根據此興趣加權關聯規則所顯示出的傾向特徵，可發掘適性化借閱此書籍的讀者。本研究根據所提出的兩個探勘方法，設計與建置一個發掘書籍借閱的適性化推薦系統。此探勘結果，對圖書館擬定書籍借閱的適性化推薦，將可以提供非常有用的參考資訊。

In this paper, readers' borrowing history records are used as the source data of mining. Each borrowing history record contains a reader once borrowed books. The study uses mining techniques with weighted interest-items to find adaptive recommendations of borrowing books from two aspects. One is to make one reader as the target of mining. The study presents a method to mine association rules with weighted interest-items whose antecedents are contained in the reader's borrowing history record. According to the characteristics of the association rules with weighted interest-items, the adaptive recommendations of borrowing books can be found for the reader. The other is to make one book as the target of mining. The study presents a method to mine association rules with weighted interest-items whose consequents are the book. According to the characteristics of the association rules with weighted interest-items, the adaptive readers can be found for borrowing the book. A mining system is designed and constructed for finding adaptive recommendations of borrowing books according to the both methods. The results of the mining can provide very useful information to plan the adaptive recommendations of borrowing books for libraries.

1. 陳垂呈(2004)。利用關聯規則發掘圖書館個人化之書籍推薦。台大圖書資訊學刊，2-2，87-103。
   連結：
2. 交通大學個人化數位圖書館資訊服務系統
3. Agrawal, R.,Imielinski, T.,Swami, A.(1993).Mining Association Rules between Sets of Items in Very Large Database.Proceedings of the ACM SIGMOD Conference on Management of Data
4. Agrawal, R.,Srikant, R.(1994).Fast Algorithms for Mining Association Rules in Large Database.Proceedings of the 20th International Conference on Very Large Data Bases
5. Berry, M. J. A.,Linoff, G. S.(2004).Data Mining Techniques for Marketing, Sales, and Customer Support.New York:John Wiley.
6. Cai, C. H.,Fu, W. C.,Cheng, C. H.,Kwong, W. W.(1998).Mining Association Rules with Weighted Items.Proceedings of the International Database Engineering and Applications Symposium (IDEAS),68-77.
7. Chen, M. S.,Han, J.,Yu, P. S.(1996).Data Mining: An Overview from a Database Perspective.IEEE Transactions on Knowledge and Data Engineering,8(6),866-883.
8. Coenen, F.,Leng, P.,Ahmed, S.(2004).Data Structure for Association Rule Mining: T-trees and P-trees.IEEE Transactions on Knowledge and Data Engineering,16(6),774-778.
9. Han, J.,Kamber, M.(2006).Data Mining: Concepts and Techniques.Morgan Kaufmann:
10. Han, J.,Pei, J.,Yin, Y.,Mao, R.(2004).Mining Frequent Patterns without Candidate Generation: a Frequent-Pattern Tree Approach.Data Mining and Knowledge Discovery,8(1),53-87.
11. Holt, J. D.,Chung, S. M.(2000).Mining Association Rules Using Inverted Hashing and Pruning.Information Processing Letters,83,211-220.
12. Ou, J.,Lin, S.,Li, J.(2001).The Personalized Index Service System in Digital Library.Proceedings of the Third International Symposium on Cooperative Database Systems for Advanced Applications
13. Park, J. S.,Chen, M. S.,Yu, P. S.(1997).Using a Hash-Based Method with Transaction Trimming for Mining Association Rules.IEEE Transactions on Knowledge and Data Engineering,9(5),813-825.
14. Tsay, Y. J.,Chang-Chien, Y. W.(2004).An Efficient Cluster and Decomposition Algorithm for Mining Association Rules.Information Sciences,160,161-171.
15. Tsay, Y. J.,Chiang, J. Y.(2005).CBAR: an Efficient Method for Mining Association Rules.Knowledge-Based Systems,18,99-105.
16. Yue, S.,Tsang, E.,Yeung, D.,Shi, D.(2000).Mining Fuzzy Association Rules with Weighted Items.Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics
17. 卜小蝶(1998)。淺析個人化服務技術的發展趨勢對圖書館的影響。國立成功大學圖書館館刊，2，63-73。
18. 吳安琪(2001)。國立交通大學資訊科學研究所碩士論文，未出版。
19. 孫冠華(2003)。南華大學資訊管理學研究所碩士論文，未出版。
20. 陳慶瑄(2000)。國立中正大學資訊管理研究所碩士論文，未出版。
21. 湯春枝(2002)。從個人化服務行銷的理念談交通大學個人化數位圖書資訊服務(PIE@NCTU)系統。國立成功大學圖書館館刊，9，33-49。
22. 辜曼蓉(1999)。讀者資訊尋求行為與以讀者為中心的圖書館行銷。書府，20，81-111。
23. 楊錦潭、陳玟志、邱魏津、朱永方(2005)。圖書館顧客關係管理與資料探勘。國家圖書館館刊，1，115-134。
24. 賴永祥(1989)。中國圖書分類法。台北:商務書局。
25. 顏秀珍、邱鼎穎、李御璽(2001)。從大型資料庫中挖掘加權的關聯規則。全國計算機會議，1，117-128。
26. 顏嘉惠(2002)。資料探勘於圖書館行銷及顧客關係管理之應用。圖書與資訊學刊，42，58-68。