Training Deep AutoEncoders for Collaborative Filtering

Oleksii Kuchaiev, Boris Ginsburg
8/5/2017
stat.MLcs.LG

Official code: NVIDIA/DeepRecommender (1,702 stars, MIT, last commit 2021-05-17).

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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Code Implementations(1)

NVIDIA/DeepRecommenderOfficial100%
MIT

Deep learning for recommender systems

1,702340PythonSep 8, 20175 years agoMIT
collaborative-filteringdeep-autoencodersdeep-learninggpurecommendation-engine

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Cite this paper

@article{kuchaiev2017training,
  title  = {Training Deep AutoEncoders for Collaborative Filtering},
  author = {Oleksii Kuchaiev and Boris Ginsburg},
  year   = {2017},
  eprint = {1708.01715},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1708.01715}
}

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