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Last Commit
Oct. 18, 2017
Created
Sep. 8, 2017

Deep AutoEncoders for Collaborative Filtering

This is not an official NVIDIA product. It is a research project described in: "Training Deep AutoEncoders for Collaborative Filtering"(https://arxiv.org/abs/1708.01715)

The model

The model is based on deep AutoEncoders.

AutEncoderPic

Requirements

  • Python 3.6
  • Pytorch
  • CUDA (recommended version is 8.0)

Getting Started

Run unittests first

The code is intended to run on GPU. Last test can take a minute or two.

$ python -m unittest test/data_layer_tests.py
$ python -m unittest test/test_model.py

Get the data

Note: Run all these commands within your DeepRecommender folder

Netflix prize

  • Download from here into your DeepRecommender folder
$ tar -xvf nf_prize_dataset.tar.gz
$ tar -xf download/training_set.tar
$ python ./data_utils/netflix_data_convert.py training_set Netflix

Data stats

Dataset Netflix 3 months Netflix 6 months Netflix 1 year Netflix full
Ratings train 13,675,402 29,179,009 41,451,832 98,074,901
Users train 311,315 390,795 345,855 477,412
Items train 17,736 17,757 16,907 17,768
Time range train 2005-09-01 to 2005-11-31 2005-06-01 to 2005-11-31 2004-06-01 to 2005-05-31 1999-12-01 to 2005-11-31
-------- ---------------- ----------- ------------
Ratings test 2,082,559 2,175,535 3,888,684 2,250,481
Users test 160,906 169,541 197,951 173,482
Items test 17,261 17,290 16,506 17,305
Time range test 2005-12-01 to 2005-12-31 2005-12-01 to 2005-12-31 2005-06-01 to 2005-06-31 2005-12-01 to 2005-12-31

Train the model

In this example, the model will be trained for 12 epochs. In paper we train for 102.

python run.py --gpu_ids 0 \
--path_to_train_data Netflix/NF_TRAIN \
--path_to_eval_data Netflix/NF_VALID \
--hidden_layers 512,512,1024 \
--non_linearity_type selu \
--batch_size 128 \
--logdir model_save \
--drop_prob 0.8 \
--optimizer momentum \
--lr 0.005 \
--weight_decay 0 \
--aug_step 1 \
--noise_prob 0 \
--num_epochs 12 \
--summary_frequency 1000

Note that you can run Tensorboard in parallel

$ tensorboard --logdir=model_save

Run inference on the Test set

python infer.py \
--path_to_train_data Netflix/NF_TRAIN \
--path_to_eval_data Netflix/NF_TEST \
--hidden_layers 512,512,1024 \
--non_linearity_type selu \
--save_path model_save/model.epoch_11 \
--drop_prob 0.8 \
--predictions_path preds.txt

Compute Test RMSE

python compute_RMSE.py --path_to_predictions=preds.txt

After 12 epochs you should get RMSE around 0.927. Train longer to get below 0.92

Results

It should be possible to achieve the following results. Iterative output re-feeding should be applied once during each iteration.

(exact numbers will vary due to randomization)

DataSet RMSE Model Architecture
Netflix 3 months 0.9373 n,128,256,256,dp(0.65),256,128,n
Netflix 6 months 0.9207 n,256,256,512,dp(0.8),256,256,n
Netflix 1 year 0.9225 n,256,256,512,dp(0.8),256,256,n
Netflix full 0.9099 n,512,512,1024,dp(0.8),512,512,n