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Restricted Boltzmann machines for collaborative filtering (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Salakhutdinov, Ruslan Hinton, Geoffrey Mnih, Andriy |
| Description | In Machine Learning, Proceedings of the Twenty-fourth International Conference (ICML 2004). ACM |
| 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. 1. |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Collaborative Filtering Inference Procedure Multiple Rbm Model User Rating Outperform Carefully-tuned Svd Model Error Rate Two-layer Undirected Graphical Model Present Efficient Learning Multiple Svd Model Netflix Data Set Tabular Data Large Data Set Restricted Boltzmann Machine Boltzmann Machine User Movie Rating |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings Article |
