Recommender agents are being widely used by E-commerce business to help customers make decisions from a large amount of choices. To improve the performance of recommendation agents, three main approaches (content-based approaches, collaborative approaches and hybrid approaches) have been proposed to address recommendation problem whose basic idea is to discover similarity of items and users and predicate users’ preference toward a set of items. This provides potential for using social network analysis to make recommendations since social network analysis can be used to investigate the relationships of customers. In this research, we illustrate the concepts of social network analysis and how it can be employed to make better recommendations in E-commerce context. Application and research opportunities are presented.
Xu, Yunhong; Ma, Jian; Sun, Yonghong; Hao, Jinxing; Sun, Yongqiang; and Zhao, Yongqiang, "USING SOCIAL NETWORK ANALYSIS AS A STRATEGY FOR E-COMMERCE RECOMMENDATION" (2009). PACIS 2009 Proceedings. 106.