@article {doi:10.1142/S0218213012500017,
	title = {APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS},
	journal = {International Journal on Artificial Intelligence Tools},
	volume = {21},
	number = {01},
	year = {2012},
	pages = {1250001},
	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{\textquoteright} 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.},
	doi = {10.1142/S0218213012500017},
	url = {https://doi.org/10.1142/S0218213012500017},
	author = {Alexandridis, Georgios and Siolas, Georgios and Stafylopatis, Andreas}
}
