Abstract
In this paper we propose a method for learning the reasons why groups of consumers prefer some food products instead of others. We emphasize the role of groups given that, from a practical point of view, they may represent market segments that demand different products. Our method starts representing people’s preferences in a metric space; there we are able to define a kernel based similarity function that allows a clustering algorithm to discover significant groups of consumers with homogeneous tastes. Finally in each cluster, we learn, with a SVM, a function that explains the tastes of the consumers grouped in the cluster. To illustrate our method, a real case of consumers of beef meat was studied. The panel was formed by 171 people who rated 303 samples of meat from 101 animals with 3 different aging periods.
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Díez, J., del Coz, J.J., Sañudo, C., Albertí, P., Bahamonde, A. (2005). A Kernel Based Method for Discovering Market Segments in Beef Meat. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_46
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DOI: https://doi.org/10.1007/11564126_46
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