ABSTRACT is paper proposes a model for the rating prediction task in recommender systems which signi cantly outperforms previous stateof-the art models on a time-split Net ix 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 be er 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 ing. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate ltering. e new algorithm signi cantly speeds up training and improves model performance. Our code is available at h ps://github.com/NVIDIA/DeepRecommender.
Furthermore, DAE has been effectively demonstrated for rating prediction tasks in recommendation systems REF .
Training Deep AutoEncoders for Collaborative Filtering
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Mathematics Computer Science