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.

Requirements

• Python 3.6
• Pytorch: pipenv install
• CUDA (recommended version >= 8.0)

Training using mixed precision with Tensor Cores

• You would need NVIDIA Volta-based GPU
• Checkout mixed precision branch
• For theory on mixed precision training see Mixed Precision Training paper

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

Tutorial

Checkout this tutorial by miguelgfierro.

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