id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_fm6xz6iburcpxgxdmde7yho7jq	Oscar Luaces	Mapping preferences into Euclidean space	2015	33	.pdf	application/pdf	7594	841	70	models than a tensorial approach that uses a SVM to learn preferences. flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an assigns a utility value to the object, or by a preference relation, which compares two different items; see (Hüllermeier and Fürnkranz, 2013). The contributions of the paper are the following: (i) it presents a common framework for different approaches to learn preferences using matrix We present a general setting that includes, at the same time, a factorization framework and a tensorial approach: a SVM that uses tensor products two different vectors, the representation of consumers and items or products approaches when used to learn consumer preferences. The framework presented in this paper includes at the same time factorization and tensorial methods; both cases use the same learning algorithm with	./cache/work_fm6xz6iburcpxgxdmde7yho7jq.pdf	./txt/work_fm6xz6iburcpxgxdmde7yho7jq.txt