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• W2743992351 abstract "This paper proposes a novel 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-fiting. 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 available at this https URL" @default.
• W2743992351 created "2017-08-17" @default.
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• W2743992351 date "2017-08-05" @default.
• W2743992351 modified "2023-09-27" @default.
• W2743992351 title "Training Deep AutoEncoders for Collaborative Filtering" @default.
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