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 is based on deep AutoEncoders.
- Python 3.6
- Pytorch:
pipenv install - CUDA (recommended version >= 8.0)
- You would need NVIDIA Volta-based GPU
- Checkout mixed precision branch
- For theory on mixed precision training see Mixed Precision Training paper
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 Checkout this tutorial by miguelgfierro.
Note: Run all these commands within your DeepRecommender folder
- Download from here into your
DeepRecommenderfolder
$ tar -xvf nf_prize_dataset.tar.gz $ tar -xf download/training_set.tar $ python ./data_utils/netflix_data_convert.py training_set Netflix | 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 |
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 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 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
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 |