Customer Need Type Classification Model using Data Mining Techniques for Recommender Systems
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.
[1] Barkuus, L, Dey, A (2003) Is context-aware computing taking control
away from the user? Three levels of interactivity examined. In:
Proceedings of the Ubicomp, pp 150-156
[2] Schilke, SW, Bleimann, U, Furnell, SM, Phippen, AD (2004)
Multi-dimensional personalisation for location and interest-based
recommendation. Internet Research 14(5):379-385
[3] Maclnnis, DJ, Jaworski, BJ (1989) Information processing from
advertisements: Toward an integrative framework. Journal of Marketing
53(4):1-23
[4] Duda R, Hart P, Stork D (2001) Pattern Classification. 2 Ed. John Wiley
and Sons, NY
[5] Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ (1984) Classification
and Regression Trees. Wadsworth & Brooks/Cole Advanced Books &
Software, Monterey, CA
[6] Kass, GV (1980) An exploratory technique for investigating large
quantities of categorical data. Applied Statistics 29(2):119-127
[7] Loh, WY, Shih, YS (1997) Split selection methods for classification trees.
Statistica Sinica 7:815-840
[8] Cooper, DR, Emory, CW (1995) Business Research Methods. Irwin, IL
[1] Barkuus, L, Dey, A (2003) Is context-aware computing taking control
away from the user? Three levels of interactivity examined. In:
Proceedings of the Ubicomp, pp 150-156
[2] Schilke, SW, Bleimann, U, Furnell, SM, Phippen, AD (2004)
Multi-dimensional personalisation for location and interest-based
recommendation. Internet Research 14(5):379-385
[3] Maclnnis, DJ, Jaworski, BJ (1989) Information processing from
advertisements: Toward an integrative framework. Journal of Marketing
53(4):1-23
[4] Duda R, Hart P, Stork D (2001) Pattern Classification. 2 Ed. John Wiley
and Sons, NY
[5] Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ (1984) Classification
and Regression Trees. Wadsworth & Brooks/Cole Advanced Books &
Software, Monterey, CA
[6] Kass, GV (1980) An exploratory technique for investigating large
quantities of categorical data. Applied Statistics 29(2):119-127
[7] Loh, WY, Shih, YS (1997) Split selection methods for classification trees.
Statistica Sinica 7:815-840
[8] Cooper, DR, Emory, CW (1995) Business Research Methods. Irwin, IL
@article{"International Journal of Business, Human and Social Sciences:54297", author = "Kyoung-jae Kim", title = "Customer Need Type Classification Model using Data Mining Techniques for Recommender Systems", 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.", keywords = "Customer need type, Data mining techniques,Recommender system, Personalization, Mobile user.", volume = "5", number = "8", pages = "984-6", }