Applying k-separability to collaborative recommender systems

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	2014-03-01T02:07:50Z
dc.date.available	2014-03-01T02:07:50Z
dc.date.issued	2012	en
dc.identifier.issn	02182130	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29611
dc.subject	boosting algorithm	en
dc.subject	collaborative recommender	en
dc.subject	constructive ANN architecture	en
dc.subject	k-separability	en
dc.subject	sparsity problem	en
dc.subject.other	Boosted learning	en
dc.subject.other	Boosting algorithm	en
dc.subject.other	Collaborative filtering	en
dc.subject.other	collaborative recommender	en
dc.subject.other	Collaborative recommender systems	en
dc.subject.other	Data sets	en
dc.subject.other	k-separability	en
dc.subject.other	Limited information	en
dc.subject.other	Noisy data	en
dc.subject.other	Sparsity problems	en
dc.subject.other	User rating	en
dc.subject.other	Network architecture	en
dc.subject.other	Neural networks	en
dc.subject.other	Quality of service	en
dc.subject.other	Recommender systems	en
dc.title	Applying k-separability to collaborative recommender systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1142/S0218213012500017	en
heal.identifier.secondary	1250001	en
heal.identifier.secondary	http://dx.doi.org/10.1142/S0218213012500017	en
heal.publicationDate	2012	en
heal.abstract	Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying Singular Value Decomposition to reduce the dimensionality of the data, our system makes use of a dynamic Artificial Neural Network architecture with boosted learning to predict user ratings. Furthermore we use the concept of k-separability to deal with the resulting noisy data, a methodology not yet tested in Recommender Systems. The combination of these techniques applied to the MovieLens datasets seems to yield promising results. © 2012 World Scientific Publishing Company.	en
heal.journalName	International Journal on Artificial Intelligence Tools	en
dc.identifier.doi	10.1142/S0218213012500017	en
dc.identifier.volume	21	en
dc.identifier.issue	1	en