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
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 |
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