臺灣博碩士論文加值系統

一般而言，顧客關係管理(CRM)對企業相當有利，而首要工作即區隔顧客，以提供客製化的產品與服務。然而，市場區隔的方式有很多，如何設計適當的區隔方法相對較為重要。再者，推薦系統即用來推薦產品給顧客，並提供相關資訊以利顧客購物。如果推薦系統特別因個人而設計，則顯得較為合適且簡潔。本研究的目的，即提供個人化產品推薦的方法，以在最適當的時機推薦最適當的產品，並針對由人口統計變數與行為變數為基礎的推薦，來評估其差異。本研究運用了兩種資料挖掘的技術：以IBM Intelligent Miner來區隔顧客，並以I-PrefixSpan演算法，在每個集群挖掘出時間間隔的序列樣式。結果指出以行為變數為基礎的推薦，比人口統計的更為準確。

It is recognized that customer relationship management (CRM) is the key point to benefit business, and the first task is to segment customers for providing customized products and services. However, the ways of market segmentation are of variety, and how to design the proper way to separate customers is more significant relatively. Also, recommender systems are used to suggest products to their customers and to provide consumers with information to help them purchase. If the recommendation is specifically designed for individuals, it will be more suitable and concise. The aims of our study are to suggest a method of personalized product recommendations to recommend appropriate products at appropriate time, and to evaluate the difference of recommendations based on demographic and behavioral segmentations. We employed two techniques of data mining: IBM Intelligent Miner to cluster the customers, and I-PrefixSpan algorithm to discover time-interval sequential patterns in every cluster. Results indicated the recommendation based on behavioral segmentation is more accurate than that based on demographic segmentation.

1INTRODUCTION1
1.1Background1
1.2Motivation and Objectives3
1.3Research Procedure4
1.4Thesis Organization5
2LITURATURE REVIEW6
2.1Overview of Data Mining6
2.1.1 Definition and characteristics of data mining6
2.1.2 Primary tasks of data mining7
2.1.3 Clustering techniques9
2.1.4 Sequential pattern mining techniques12
2.1.5 Illustration of I-PrefixSpan13
2.1.6 IBM Intelligent Miner16
2.2Development of Recommender System19
2.2.1 Content-based filtering19
2.2.2 Collaborative filtering20
2.2.3 Taxonomy for applications to recommender system22
2.3Target Marketing24
2.3.1 Market segmentation variables24
2.3.2 RFM analysis26
3METHODOLOGY28
3.1Research Framework28
3.2Data Sets30
3.3Product Taxonomy32
3.4Customer Segmentation by Clustering33
3.5Discovery of Time-Interval Sequential Patterns37
4RECOMMENDATION BY DATA MINING TECHNIQUES39
4.1Clusters Produced by Demographic Clustering Technique39
4.2Clusters Produced by Neural Clustering Technique45
4.3Time-Interval Sequential Patterns Discovered by I-PrefixSpan48
4.4Personalized Product Recommendations50
5COMPARISON AND CONTRAST51
6DISCUSSION AND LIMITATION53
REFERENCES55

[1]R. Agrawal and R. Srikant, “Mining sequential patterns,” in 11th International Conference Data Engineering (ICDE’95), pp. 3-14, 1995.
[2]M. Balabanovic and Y. Shoham, “Fab: content-based, collaborative recommendation,” Communications of the ACM (CACM), vol. 40(3), pp. 66-72, March 1997.
[3]C. Baragoin, C. M. Andersen, S. Bayerl, G. Bent, J. Lee, and C. Schommer, Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data, IBM Redbooks, 1st edition, Aug 2001.
[4]R. C. Blattberg and S. K. Sen, “Market segmentation using models of multidimensional purchasing behavior,” Journal of Marketing, vol. 38, pp. 17-28, October 1974.
[5]P. Cabena, H. H. Choi, I. S. Kim, S. Otsuka, J. Reinschmidt, G. Saarenvirta, Intelligent Miner for Data Applications Guide, IBM Redbooks, 1st edition, March 1999.
[6]Y. L. Chen, M. C. Chiang, and M. T. Kao, “Discovering time-interval sequential patterns in sequence databases,” accepted in Expert Systems with Applications (SCI), 2003.
[7]Y. H. Cho, J. K. Kim, and S. H. Kim, “A personalized recommender system based on web usage mining and decision tree induction,” Expert Systems with Applications, vol. 23, pp. 329-342, 2002.
[8]R. Cooley, B. Mobasher, and J. Sricastava, “Data preparation for mining world wide web browsing patterns,” Journal of Knowledge and Information Systems, vol. 1, no. 1, pp. 5-32, 1999.
[9]S. Dibb and L. Simkin, The Market Segmentation Workbook: Target Marketing for Marketing Managers, Routledge, 1st edition, 1996.
[10]U. M. Fayyad, G. P. Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining, MA: AAAI/MIT Press, pp. 471-493, 1996.
[11]Y. Fu, “Data mining: tasks, techniques and application,” IEEE Potentials, vol. 16, pp. 18-20, Oct/Nov 1997.
[12]D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35(12), pp. 61-70, December 1992.
[13]A. W. Hafner, “Pareto’s Principle: The 80-20 Rule,” http://www.bsu.edu/libraries/ ahafner/awh-th-math-pareto.htm, March 31, 2001.
[14]O. P. Hall, “Mining the store,” The Journal of Business Strategy, vol. 22, pp. 24-27, Mar/Apr 2001.
[15]J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 1st edition, 2001.
[16]J. Han, W. Gong, and Y. Yin, “Mining segment-wise periodic patterns in time- related database,” in International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 214-218, 1998.
[17]W. Hill and L. Terveen, “Using frequency-of-mention in public conversations for social filtering,” in Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW ''96), pp. 106-112, November 1996.
[18]W. Hill, L. Stead, M. Rosenstein, and G. Furnas, “Recommending and Evaluating Choices in a Virtual Community of Use,” in Proceedings of the Conference on Human Factors in Computing Systems (CHI ''95), pp. 194-201, 1995.
[19]A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice-Hall Advanced Reference Series: Computer Science, March 1988.
[20]D. Jenkins, “Customer relationship management and the data warehouse,” Call Center Management Solutions, vol. 18, pp. 88-91, Aug 1999.
[21]R. Kahan, “Using database marketing techniques to enhance your one-to-one marketing initiatives,” The Journal of Consumer Marketing, vol. 15(5), pp. 491-493, 1998.
[22]T. Kohonen, Self-Organizing Maps, Springer-Verlag, 1995.
[23]J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, “GroupLens: applying collaborative filtering to Usenet news,” Communications of the ACM (CACM), vol. 40(3), pp. 77-87, March 1997.
[24]P. Kotler, S. H. Ang, S. M. Leong, and C. T. Tan, Marketing Management: An Asian Perspective, Prentice Hall, 2nd edition, pp. 272-281, November 1999.
[25]R. D. Lawrence, G. S. Almasi, V. Kotlyar, M. S. Viveros, S.S. Duri, “Personalization of supermarket product recommendations,” Data Mining and Knowledge Discovery, vol. 5, pp. 11-32, 2001.
[26]C. S. Li, P. S. Yu, and V. Castelli, “HierarchyScan: A hierarchical similarity search algorithm for databases of long sequences,” in 12th International Conference Data Engineering (ICDE’96), pp. 546-553, 1996.
[27]A. L. Majurin, Industrial Segmentation: A Review, Master’s Thesis, School of Business, Åbo Akademi University, Finland, 2001.
[28]H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovery of frequent episodes in event sequences,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 259-289, 1997.
[29]P. Michaud, “Clustering techniques,” Future Generation Computer Systems, vol. 13(2), pp. 135-147, November 1999.
[30]B. Mobasher, R. Cooley, and J. Srivastava, “Automatic personalization based on web usage mining,” Communications of the ACM (CACM), vol. 43(8), pp. 142-151, 2000.
[31]P. R. Peacock, “Data mining in marketing: part 1,” Marketing Management, vol. 6, pp. 8-18, winter 1998.
[32]P. R. Peacock, “Data mining in marketing: part 2,” Marketing Management, vol. 7, pp. 14-25, spring 1998.
[33]J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M. C. Hsu, “PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth”, in 17th International Conference in Data Engineering (ICDE’01), pp. 215-224, 2001.
[34]D. Peppers and M. Rogers, The One to One Future: Building Relationships One Customer at a Time, Currency/Doubleday Publishers, pp. 95-173, January 1997.
[35]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” in Proceedings of the ACM Conference on Computer-Supported Cooperative Work, pp. 175-186, 1994.
[36]D. E. Rumelhart and D. Zipser, “Feature discovery by competitive learning,” Cognitive Science, vol. 9, pp. 75-112, 1985.
[37]B. Sarwar, G. Karypis, J. Konstan, J. Riedl, “Analysis of recommendation algorithms for e-commerce,” in Proceedings of ACM E-Commerce 2000 Conference, pp. 158-167, 2000.
[38]B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of dimensionality reduction in recommender systems: a case study,” in ACM WebKDD 2000 Web Mining for E-Commerce Workshop, Boston, MA, 2000.
[39]J. B. Schafer, J. A. Konstan, and J. Riedl, “E-commerce recommendation applications”, Data Mining and Knowledge Discovery, vol. 5, pp. 115-153, 2001.
[40]J. B. Schafer, J. A. Konstan, and J. Riedl, “Recommender systems in e-commerce,” in Proceedings of the ACM Conference on Electronic Commerce (EC’99), pp. 158-166, Nov. 1999.
[41]U. Shardanand and P. Maes, “Social information filtering: algorithms for automating ‘word of mouth’,” in Proceedings of the Conference on Human Factors in Computing Systems (CHI ''95), pp. 210-217, 1995.
[42]R. Srikant and R. Agrawal, “Mining sequential patterns: generalizations and performance improvements,” in 5th International Conference on Extending Database Technology (EDBT’96), pp. 3-17, 1996.
[43]P. H. Wu, W. C. Peng, and M. S. Chen, “Mining sequential alarm patterns in a telecommunication database,” Databases in Telecommunications (VLDB 2001), pp. 37-51, 2001.
[44]G. A. Wyner, “Segmentation design,” Marketing Research, vol. 4, pp. 38-41, December 1992.