인터넷 쇼핑몰 사용자가 구축하는 연결망 구조분석을 통한 구매행동 모델링

As e-commerce based market is getting popular, classifying customers into groups and predicting the decision of whether customers buy products or not in web stores provide useful information for the web service providers. This study proposes an approach that classifies groups of customer using clickstream data and network theory. Specifically, a user’s session from clickstream dataset can be transformed into a graphical network, which represented in density and centralization based on graph theory. The history of online users' activities can be used to construct a relationship network graphically. Dataset is formed on the basis of a session which means a sequence of page views or a period of sustained web browsing. The sessions based on density and centralization of a graph are evaluated into logistic regression. The results show that the proposed model including density and centralization of the session is wellperformed in classifying groups. This study shows customers’ session of dataset with respect to a graph structure can be used to predict whether a customer will buy or not buy a product in a web store. This study proposes an approach to identify customers who show high possibility in purchasing using data set on their behaviors in the internet decision space.