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Using a Clustering Genetic Algorithm to Support Customer Segmentation for Personalized Recommender Systems

  • Kyoung-jae Kim
  • Hyunchul Ahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3397)

Abstract

This study proposes novel clustering algorithm based on genetic algorithms (GAs) to carry out a segmentation of the online shopping market effectively. In general, GAs are believed to be effective on NP-complete global optimization problems and they can provide good sub-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters. This paper applies GA-based K-means clustering to the real-world online shopping market segmentation case for personalized recommender systems. In this study, we compare the results of GA-based K-means to those of traditional K-means algorithm and self-organizing maps. The result shows that GA-based K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms.

Keywords

Cluster Algorithm Recommender System Initial Seed Market Segmentation Uniform Crossover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-means Clustering. In: Proc. of the 15th International Conference on Machine Learning, pp. 91–99 (1998)Google Scholar
  2. 2.
    Dibb, S., Simkin, L.: The Market Segmentation Workbook: Target Marketing for Marketing Managers, Routledge, London (1995)Google Scholar
  3. 3.
    Gehrt, K.C., Shim, S.: A Shopping Orientation Segmentation of French Consumers: Implications for Catalog Marketing. J. of Interactive Marketing 12(4), 34–46 (1998)CrossRefGoogle Scholar
  4. 4.
    Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory Neural Computation. Addison-Wesley, Reading (1991)Google Scholar
  5. 5.
    Kehoe, C., Pitkow, J., Rogers, J.D.: Ninth GVU.s WWW User Survey (1998), http://www.gvu.gatech.edu/user_surveys/survey-1998-04/
  6. 6.
    Kim, K.: Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting. Applied Intelligence (2004) (forthcoming)Google Scholar
  7. 7.
    Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59–69 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Michaud, P.: Clustering Techniques. Future Generation Computer Systems 13, 135–147 (1997)CrossRefGoogle Scholar
  9. 9.
    Shin, H.W., Sohn, S.Y.: Segmentation of Stock Trading Customers According to Potential Value. Expert Systems with Applications 27(1), 27–33 (2004)CrossRefGoogle Scholar
  10. 10.
    Shin, K.S., Han, I.: Case-Based Reasoning Supported by Genetic Algorithms for Corporate Bond Rating. Expert Systems with Applications 16, 85–95 (1999)CrossRefGoogle Scholar
  11. 11.
    Velido, A., Lisboa, P.J.G., Meehan, K.: Segmentation of the On-Line Shopping Market using Neural Networks. Expert Systems with Applications 17, 303–314 (1999)CrossRefGoogle Scholar
  12. 12.
    Wong, F., Tan, C.: Hybrid Neural, Genetic and Fuzzy Systems. In: Deboeck, G.J. (ed.) Trading On The Edge, pp. 243–261. Wiley, New York (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kyoung-jae Kim
    • 1
  • Hyunchul Ahn
    • 2
  1. 1.Department of Information SystemsDongguk UniversitySeoulKorea
  2. 2.Graduate School of ManagementKorea Advanced Institute of Science and TechnologySeoulKorea

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