TY - JOUR
T1 - A recommender system using GA K-means clustering in an online shopping market
AU - Kim, Kyoung jae
AU - Ahn, Hyunchul
PY - 2008/2
Y1 - 2008/2
N2 - The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers' needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
AB - The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers' needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
KW - Case-based reasoning
KW - Genetic algorithms
KW - Market segmentation
KW - Recommender system
KW - Self-organizing maps
UR - http://www.scopus.com/inward/record.url?scp=36148984621&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2006.12.025
DO - 10.1016/j.eswa.2006.12.025
M3 - Article
AN - SCOPUS:36148984621
SN - 0957-4174
VL - 34
SP - 1200
EP - 1209
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 2
ER -