@article{(Open Science Index):https://publications.waset.org/pdf/5197,
	  title     = {Customer Need Type Classification Model using Data Mining Techniques for Recommender Systems},
	  author    = {Kyoung-jae Kim},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {5},
	  number    = {8},
	  year      = {2011},
	  pages     = {973 - 978},
	  ee        = {https://publications.waset.org/pdf/5197},
	  url   	= {https://publications.waset.org/vol/56},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 56, 2011},
	}