Deep learning

Organic chemistry

Restricted Boltzmann machine

Collaborative filtering

Inference

Recommender system

Data mining

Machine learning

Artificial intelligence

Graphical model

Boltzmann machine

Set (abstract data type)

Chemistry

Programming language

Class (philosophy)

Layer (electronics)

Computer science

Data set

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.

Restricted Boltzmann machines for collaborative filtering