Author(s)

William K. Cheung, James T. Kwok, Martin H. Law & Kwok-Ching Tsui

Abstract

As Internet commerce becomes more popular, customers' preferences on var- ious products can now be readily acquired on-line via various e-commerce systems. Properly mining this extracted data can generate useful knowledge for providing personalized product recommendation services. In general, recommender systems use two complementary techniques. Content-based systems match customer interests with products attributes, while collabo- rative filtering systems utilize preference ratings from other customers. In this paper, we address some problems faced by these two syste

Keywords