針對特定群體之需求擬定產品及行銷策略長久以來為企業所重視，為了達到更有效率的市場區隔，本研究提出一個新的方法來推薦產品。剛開始先從交易紀錄資料中找出顧客屬性與產品類別經常一起時出現的關聯法則，其中顧客屬性與產品類別都包含了階層的概念。然後根據之前找到的關聯法則，在給定推薦產品情況下，利用演算法來找出目標顧客。方法評估上，先從校內圖書館的OPAC System 取得讀者的借閱紀錄資料，藉由調整不同參數分別衡量預測的正確性與目標行銷的有效性，結果顯示跟其他方法相比，可以達到較高的預測的正確性與推薦有效性。

Targeting a small portion of customers for advertising has long been recognized by businesses. In this thesis we proposed a novel approach to promoting products with no prior transaction records. This approach starts with discovering the GP-association rules between customer types and product genres that had occurred frequently in transaction records. Customers are characterized by demographic attributes, some of these attributes have concept hierarchies and products can be generalized through some product taxonomy. Based on GP-association rules set, we developed a comprehensive algorithm to locating a short list of prospective customers for a given promotion product. The new approach was evaluated using the patron’s circulation data from OPAC system of our university library. We measured the accuracy of estimated method and the effectiveness of targeted advertising in different parameters. The result shows that our approach achieved higher accuracy and effectiveness than other methods.

Table of Content

Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivations and Objectives 3
1.3 Thesis Organization 6
Chapter 2 Literature review 7
2.1 GP-association rules 7
2.1.1 Extended transaction 7
2.1.2 GP-Apriori algorithm 9
2.2 Estimating specialized rules from generalized rules 12
2.3 Scheduling of web advertisements 15
2.4 Optimized Association Rules 17
Chapter 3 Targeted Advertising 21
3.1 Problem Statement 21
3.2 Value estimation 22
3.3 Identifying applicable demographics of a GP-association rule 25
3.4 Identifying disjoint demographic segments 28
Chapter 4 Identifying Targeted Customers 30
Chapter 5 Performance evaluations 33
5.1 Accuracy of estimated confidence 34
5.2 Effectiveness for targeted advertising 38
Chapter 6 Conclusions 42
References 43

[AGM02] M. Adler, P. B. Gibbsons, Y. Martia, “Scheduling space sharing for Internet advertising,” Journal of Scheduling, 5(2), pp. 103-119, 2002.
[AIS93] R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington DC, pp. 207–216, 1993.
[AM03] A. Amiri and S. Menon, “Efficient Scheduling of internet banner advertisements,” ACM Transactions on Internet Technology, 3(4), November, pp. 334-346, 2003.
[AS94] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” Proceedings of the 20th VLDB Conference, pp. 478–499, Sept. 1994.
[AS95] R. AgrawaI and R. Srikant, “Mining sequential patterns,” Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, March 1995.
[ASY98] C. Aggarwal, Z. Sun and P. S. Yu, “Online algorithms for finding profile association rules,” Proceedings of Fifth International Conference on Information and Knowledge Management (CIKM98), pp. 86–95, 1998.
[AT01] G. Adomavicius and A. Tuzhilin, “Multidimensional recommender systems: A data warehousing approach,” Proceedings of Second International Workshop on Electronic Commerce (WELCOM01), Heidelberg, Germany, 2001.
[BL98] J. P. Benway and D. M Lane, “Banner blindness: Web searchers often miss obvious links,” Internetworking, 1(3), 1998, also available at http://www.internettg.org/newsletter/dec98/banner_blindness.html.
[FTTM96] T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, “Mining optimized association rules from numeric attributes,” Proceeding of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1996.
[HC80] R. V. Hogg and A. T. Craig, Introduction to Mathematical Statistics, 4’th ed., MacMillan Publishing, New York, 1980.
[HF95] J. Han and Y. Fu, “Discovery of multiple-level association rules from large databases,” Proceedings of the 21st VLDB Conference, pp. 420–431, 1995.
[HL02] S. Hwang and E. Lim, “A data mining approach to new library book recommendations,” International Conference on Asian Digital Libraries, 2002.
[HT97] R. V. Hogg and E. A. Tanis, Probability and Statistical Inference, 5’th ed., Prentice-Hall, N.J., 1997.
[KMRTV94] M. Klemettinen, H. ManniIa, P. Ronkainen, H. Toivonen, and A. I. Verkamo, “Finding interesting rules from large sets of discovered association rules,” Processing 3rd Int’l Conf. on Information and Knowledge Management, pp. 401-408, Gaithersburg, Maryland, Nov. 1994.
[LNAK99] M. Langheinrich, A. Nakamura, N. Abe, T. Kamba, and Y. Koseki, “Unintrusive customization techniques for web advertising,” Computer Networks, 31, pp. 1259-1272, 1999, also available on Proceedings of the 8’th International World Wide Web Conference (WWW1999)
[Naka02] A. Nakamura, “Improvements in practical aspects of optimally scheduling web advertising,” Proceedings of the 11’th International World Wide Web Conference (WWW2002), pp. 536-541, 2002.
[PCY95] J.S. Park, M.S. Chen, and P.S. Yu, “An effective hash based algorithm for mining association rules,” Proceeding 1995 ACM-SIGMOD Int. Conf. Management of Data, San Jose, CA, May 1995
[PM94] G. Piatesky-Shapiro and C. J. Matheus. “The interestingness of deviations,” In AAAI’94 Workshop on Knowledge Discovery in Databases, Seattle, pp. 25-36, WA, July 1994.
[RS98] R. Rastogi and K. Shim, “Mining Optimized Association Rules with Categorical and Numeric Attributes,” Proceedings of the International Conference on Data Engineering, Orlando, Florida, February 1998.
[SA95] R. Srikant and R. Agrawal, “Mining generalized association rules,” Proceedings of the 21st VLDB Conference, Zurich, Switzerland, pp. 409–419, Sept. 1995.
[SA96] R. Srikant and R. Agrawal, “Mining quantitative association rules in large tables,” Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, pp. 1–12, June 1996.
[SD02] T. Soukup and I. Davidson, Visual Data Mining: Techniques and Tools for Data Visualization and Mining, (to be published by) Wiley, 2002.
[SPAM03] Spam laws, see http://www.spamlaws.com/.
[Toml00] J. A. Tomlin, “An entropy approach to unintrusive targeted advertising on the Web,” Computer Networks 33, pp. 767-774, 2000, also available on the Proceedings of the 11’th International World Wide Web Conference (WWW2000).
[WSJ94] “Using Computers to Divine Who Might Buy a Gas Grill,” The Wall Street Journal, August 16, 1994.