Restricted Boltzmann machines for collaborative filtering Export

@inproceedings{@inproceedings{1273596, author = {Salakhutdinov, Ruslan and Mnih, Andriy and Hinton, Geoffrey}, title = {Restricted Boltzmann machines for collaborative filtering}, booktitle = {ICML '07: Proceedings of the 24th international conference on Machine learning}, year = {2007}, isbn = {978-1-59593-793-3}, pages = {791--798}, location = {Corvalis, Oregon}, doi = {http://doi.acm.org/10.1145/1273496.1273596}, publisher = {ACM}, address = {New York, NY, USA}, }, abstract = {Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM's can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM's slightly outperform carefully-tuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6\% better than the score of Netflix's own system.}, address = {New York, NY, USA}, author = {Salakhutdinov, Ruslan and Mnih, Andriy and Hinton, Geoffrey}, booktitle = {ICML '07: Proceedings of the 24th international conference on Machine learning}, citeulike-article-id = {1639680}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1273496.1273596}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1273496.1273596}, doi = {10.1145/1273496.1273596}, isbn = {978-1-59593-793-3}, keywords = {boltzmann\_machines, collaborative\_filtering}, location = {Corvalis, Oregon}, pages = {791--798}, posted-at = {2009-06-26 04:02:37}, priority = {2}, publisher = {ACM}, title = {Restricted Boltzmann machines for collaborative filtering}, url = {http://dx.doi.org/10.1145/1273496.1273596}, year = {2007} }

TY - CONF ID - @inproceedings{1273596, author = {Salakhutdinov, Ruslan and Mnih, Andriy and Hinton, Geoffrey}, title = {Restricted Boltzmann machines for collaborative filtering}, booktitle = {ICML '07: Proceedings of the 24th international conference on Machine learning}, year = {2007}, isbn = {978-1-59593-793-3}, pages = {791--798}, location = {Corvalis, Oregon}, doi = {http://doi.acm.org/10.1145/1273496.1273596}, publisher = {ACM}, address = {New York, NY, USA}, } L3 - citeulike-article-id:1639680 N2 - Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM's can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM's slightly outperform carefully-tuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6% better than the score of Netflix's own system. CY - Corvalis, Oregon SN - 978-1-59593-793-3 AD - New York, NY, USA EP - 798 TI - Restricted Boltzmann machines for collaborative filtering PB - ACM T2 - ICML '07: Proceedings of the 24th international conference on Machine learning SP - 791 KW - boltzmann_machines KW - collaborative_filtering AU - Salakhutdinov, Ruslan AU - Mnih, Andriy AU - Hinton, Geoffrey PY - 2007/// UR - http://dx.doi.org/10.1145/1273496.1273596 ER -