An efficient collaborative recommender system based on k-separability

Alexandridis, G; Siolas, G; Stafylopatis, A

dc.contributor.author	Alexandridis, G	en
dc.contributor.author	Siolas, G	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:46:40Z
dc.date.available	2014-03-01T02:46:40Z
dc.date.issued	2010	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32778
dc.subject	recommender system	en
dc.subject	User Preferences	en
dc.subject	Neural Network	en
dc.subject.other	Collaborative recommender systems	en
dc.subject.other	Constructive neural network algorithm	en
dc.subject.other	Limited information	en
dc.subject.other	matrix	en
dc.subject.other	Recommender systems	en
dc.subject.other	User interfaces	en
dc.subject.other	Neural networks	en
dc.title	An efficient collaborative recommender system based on k-separability	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-15825-4_25	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-15825-4_25	en
heal.publicationDate	2010	en
heal.abstract	Most recommender systems usually have too many items to recommend to too many users using limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This article outlines a collaborative recommender system, that tries to amend this situation. The system is built around the notion of k-separability combined with a constructive neural network algorithm. © 2010 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-15825-4_25	en
dc.identifier.volume	6354 LNCS	en
dc.identifier.issue	PART 3	en
dc.identifier.spage	198	en
dc.identifier.epage	207	en