This paper proposes a new model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demonstrate that: a) deep autoencoder models generalize much better than the shallow ones, b) non-linear activation functions with negative parts are crucial for training deep models, and c) heavy use of regularization techniques such as dropout is necessary to prevent over-fitting. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering. The new algorithm significantly speeds up training and improves model performance. Our code is publicly available.
This paper demonstrates how to train deep autoencoders end-to-end to achieve SoA results on time-split Netflix data set. This paper presents a deep autoencoder model for rating prediction that outperforms other state-of-the-art approahces on the Netflix prize dataset.  Proposes to use a deep AE to do rating prediction tasks in recommender systems. The authors present a model for more accurate Netflix recommendations demonstrating that a deep autoencoder can out-perform more complex RNN-based models that have temporal information. 