%0 Generic %1 nguyen2018matrix %A Nguyen, Duc Minh %A Tsiligianni, Evaggelia %A Deligiannis, Nikos %D 2018 %K matrix_factorization %T Matrix Factorization via Deep Learning %U http://arxiv.org/abs/1812.01478 %V abs/1812.01478 %X Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; and (ii) their results are often degraded in case discrete predictions are required. This paper addresses these two drawbacks by presenting a deep matrix factorization model and a generic method to allow joint training of the factorization model and the discretization operator. Experiments on a real movie rating dataset show the efficacy of the proposed models.

@conference{nguyen2018matrix, abstract = {Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; and (ii) their results are often degraded in case discrete predictions are required. This paper addresses these two drawbacks by presenting a deep matrix factorization model and a generic method to allow joint training of the factorization model and the discretization operator. Experiments on a real movie rating dataset show the efficacy of the proposed models.}, added-at = {2019-12-06T10:22:02.000+0100}, author = {Nguyen, Duc Minh and Tsiligianni, Evaggelia and Deligiannis, Nikos}, biburl = {https://www.bibsonomy.org/bibtex/28bd95ec345b13557eed78119f870ef4d/cyn7hia}, description = {Matrix Factorization via Deep Learning.pdf}, interhash = {f73b044271dd541d35b5286d9075e77a}, intrahash = {8bd95ec345b13557eed78119f870ef4d}, keywords = {matrix_factorization}, note = {cite arxiv:1812.01478Comment: in Proceedings of iTWIST'18, Paper-ID: 27, Marseille, France, November, 21-23, 2018}, timestamp = {2019-12-09T08:42:51.000+0100}, title = {Matrix Factorization via Deep Learning}, url = {http://arxiv.org/abs/1812.01478}, volume = {abs/1812.01478}, year = 2018 }