@conference {10.1007/978-3-642-15825-4_25,
	title = {An Efficient Collaborative Recommender System Based on k-Separability},
	booktitle = {Artificial Neural Networks {\textendash} ICANN 2010},
	year = {2010},
	pages = {198{\textendash}207},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	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{\textquoteright} 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.},
	isbn = {978-3-642-15825-4},
	author = {Alexandridis, Georgios and Siolas, Georgios and Stafylopatis, Andreas},
	editor = {Diamantaras, Konstantinos and Duch, Wlodek and Iliadis, Lazaros S.}
}
