TY - JFULL AU - Kyoung-jae Kim PY - 2011/9/ TI - Customer Need Type Classification Model using Data Mining Techniques for Recommender Systems T2 - International Journal of Economics and Management Engineering SP - 972 EP - 978 VL - 5 SN - 1307-6892 UR - https://publications.waset.org/pdf/5197 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 56, 2011 N2 - Recommender systems are usually regarded as an important marketing tool in the e-commerce. They use important information about users to facilitate accurate recommendation. The information includes user context such as location, time and interest for personalization of mobile users. We can easily collect information about location and time because mobile devices communicate with the base station of the service provider. However, information about user interest can-t be easily collected because user interest can not be captured automatically without user-s approval process. User interest usually represented as a need. In this study, we classify needs into two types according to prior research. This study investigates the usefulness of data mining techniques for classifying user need type for recommendation systems. We employ several data mining techniques including artificial neural networks, decision trees, case-based reasoning, and multivariate discriminant analysis. Experimental results show that CHAID algorithm outperforms other models for classifying user need type. This study performs McNemar test to examine the statistical significance of the differences of classification results. The results of McNemar test also show that CHAID performs better than the other models with statistical significance. ER -