Training Deep AutoEncoders for Collaborative Filtering

Oleksii Kuchaiev; Boris Ginsburg

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arXiv · 1708.01715

Training Deep AutoEncoders for Collaborative Filtering

Oleksii Kuchaiev·Boris Ginsburg

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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 https://github.com/NVIDIA/DeepRecommender

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Oleksii Kuchaiev, Boris Ginsburg. 2017-08-05. Training Deep AutoEncoders for Collaborative Filtering. https://arxiv.org/abs/1708.01715

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