Availableonlineatwww.sciencedirectcom scⅰ enceDirect Expert Sy stems with Applications ELSEVIER Expert Systems with Applications 34(2008)1200-1209 www.elsevier.com/locate/eswa A recommender system using Ga K-means clustering in an online shopping market Kyoung-jae Kim , Hyunchul Ahn Graduate School of Management, Korea Adranced Institute of Science and Technology, 207-43 Cheongrangri-Dong Dongdaemun-Gu, Seoul 130-722, South Korea Abstract The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers'needs and expectations 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 seg entation 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 system o 2007 Elsevier Ltd. All rights reserved. Keywords: Recommender system; Genetic algorithms; Self-organizing maps; Market segmentation; Case-based reasoning 1. Introduction classes in which the consumers can be naturally grouped, according to the information available(velido, Lisboa, Since the Internet has become popular, the consumer- Meehan, 1999). It can be the basis for effective targeting oriented electronic commerce market has grown so huge and predicting prospects through the identification of the that now companies are convinced of the importance of proper segments. Although much of the marketing litera- more important for the companies to analyze and under- clustering techniques are frequently used in practice Wear understanding this new emerging market. It is becoming ture has proposed various market segmentation techniques, stand the needs and expectations of their online users or Kamakura, 1998). In addition, K-means clustering customers because the Internet is one of the most effective the most frequently used market segmentation technique media to gather, disseminate and utilize the information among the clustering techniques( Gehrt Shim, 1998; about its users or customers. Thus, it is easier to extract Kuo, Chang, Chien, 2004). However, the major draw- nowledge out of the shopping process to create new busi- back of K-means clustering is that it often falls in local ness opportunities under the Internet environment optima and the result largely depends on the initial cluster 1 Market segmentation is one of the ways in which such centers. Prior studies pointed out this limitation and tried owledge can be represented. It attempts to discover the to integrate K-means clustering and global search tech- niques including genetic algorithms(see Babu Murty 1993: Kuo, Liao, Tu, 2005: Maulik Bandyopadhyay Corresponding author. Tel: +822 2260 3324: fax: +822 2260 3684. 2000; Murthy chowdhury, 1996; Pena, Lozano, lar- E-mail address: kjkim(@dongguk. edu(K -j. Kim). sanaga, 1999) 0957-4174/S- see front matter 2007 Elsevier Ltd. All rights reserved doi:10.1016/eswa200612025A recommender system using GA K-means clustering in an online shopping market Kyoung-jae Kim a,*, Hyunchul Ahn b a Department of Management Information Systems, Dongguk University, 3-26 Pil-Dong, Jung-Gu, Seoul 100-715, South Korea b Graduate School of Management, Korea Advanced Institute of Science and Technology, 207-43 Cheongrangri-Dong, Dongdaemun-Gu, Seoul 130-722, South Korea Abstract 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 seg￾mentation 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. 2007 Elsevier Ltd. All rights reserved. Keywords: Recommender system; Genetic algorithms; Self-organizing maps; Market segmentation; Case-based reasoning 1. Introduction Since the Internet has become popular, the consumer￾oriented electronic commerce market has grown so huge that now companies are convinced of the importance of understanding this new emerging market. It is becoming more important for the companies to analyze and under￾stand the needs and expectations of their online users or customers because the Internet is one of the most effective media to gather, disseminate and utilize the information about its users or customers. Thus, it is easier to extract knowledge out of the shopping process to create new busi￾ness opportunities under the Internet environment. Market segmentation is one of the ways in which such knowledge can be represented. It attempts to discover the classes in which the consumers can be naturally grouped, according to the information available (Velido, Lisboa, & Meehan, 1999). It can be the basis for effective targeting and predicting prospects through the identification of the proper segments. Although much of the marketing litera￾ture has proposed various market segmentation techniques, clustering techniques are frequently used in practice (Wedel & Kamakura, 1998). In addition, K-means clustering is the most frequently used market segmentation technique among the clustering techniques (Gehrt & Shim, 1998; Kuo, Chang, & Chien, 2004). However, the major draw￾back of K-means clustering is that it often falls in local optima and the result largely depends on the initial cluster centers. Prior studies pointed out this limitation and tried to integrate K-means clustering and global search tech￾niques including genetic algorithms (see Babu & Murty, 1993; Kuo, Liao, & Tu, 2005; Maulik & Bandyopadhyay, 2000; Murthy & Chowdhury, 1996; Pena, Lozano, & Lar￾ranaga, 1999). 0957-4174/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2006.12.025 * Corresponding author. Tel.: +82 2 2260 3324; fax: +82 2 2260 3684. E-mail address: kjkim@dongguk.edu (K.-j. Kim). www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 34 (2008) 1200–1209 Expert Systems with Applications