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)


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.


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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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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