Training Deep AutoEncoders for Collaborative Filtering

Code

• github.com/NVIDIA/DeepRecommender
  OfficialIn paperpytorch★ 1,702
• github.com/JerryKwon/melon_plylst_continuation
  tf★ 1
• github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deeprec
  paddle★ 0
• github.com/marlesson/recsys_autoencoders
  tf★ 0
• github.com/2023-MindSpore-1/ms-code-220/tree/main/DIEN
  mindspore★ 0
• github.com/butroy/movie-autoencoder
  tf★ 0
• github.com/Chinmayrane16/DeepRecommender
  pytorch★ 0
• github.com/chenjiyan2001/Paddle-DeepRecommender
  paddle★ 0
• github.com/suvigyavijay/DeepRecommender
  tf★ 0
• github.com/yrbahn/Deep-AutoEncoders-for-Collaborative-Filtering
  tf★ 0

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

Tasks

Reproductions