A recommender system using GA K-means clustering in an online shopping market

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 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.