Mining customer preference ratings for product recommendation using the support vector machine and the latent class model
As Internet commerce becomes more popular, customers' preferences on various 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 collaborative filtering systems utilize preference ratings from other customers. In this paper, we address some problems faced by these two systems, and study how machine learning techniques, namely the support vector machine and the latent class model, can be used to alleviated them.