id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_3yrlw3wgnjbahm23qkojfkuotu	J DIEZ	Clustering people according to their preference criteria	2008	27	.pdf	application/pdf	8267	865	72	preference judgments, can induce ranking functions that map objects into real numbers, in such a way that people's preferences in a metric space, where it is possible to define a kernel based similarity function; proposal is to use the ranking functions induced from the preference judgments of each person; we will Keywords: learning preferences, clustering, adaptive assistants, analysis of sensory data, market In this paper we tackle a slightly different problem: learning people's preferences for consumable In this paper we present a new algorithm that, given a family of preference judgment sets from a number similarity can be defined between the ranking functions learned from the preference judgment set of each represents a product rated by a consumer in a given tasting session. character of people's preferences, for each consumer pi, we will separate the ratings given at each tasting The data collects the sensory ratings of a panel of beef meat consumers about two 	./cache/work_3yrlw3wgnjbahm23qkojfkuotu.pdf	./txt/work_3yrlw3wgnjbahm23qkojfkuotu.txt