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
• 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

Netflix prize

• $ mkdir -p ~/Recommendations you can use any other folder name
• Download from here to ~/Recommendations
• $ cd ~/Recommendations
• $ tar -xvf nf_prize_dataset.tar.gz
• $ tar -xf download/training_set.tar
• Create necessary foldersmkdir -p Netflix/N3M_TRAINmkdir -p Netflix/N3M_VALIDmkdir -p Netflix/N3M_TESTmkdir -p Netflix/N6M_TRAINmkdir -p Netflix/N6M_VALIDmkdir -p Netflix/N6M_TESTmkdir -p Netflix/N1Y_TRAINmkdir -p Netflix/N1Y_VALIDmkdir -p Netflix/N1Y_TESTmkdir -p Netflix/NF_TRAINmkdir -p Netflix/NF_VALIDmkdir -p Netflix/NF_TEST
• $ python ~/repos/DeepRecoEncoders/data_utils/netflix_data_convert.py training_set Netflix. Here ```~/repos/DeepRecoEncoders''' is a path to this repo.

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 ~/repos/DeepRecoEncoders/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 ~/repos/DeepRecoEncoders/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 ~/repos/DeepRecoEncoders/compute_RMSE.py --path_to_predictions=preds.txtAfter 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 appliedonce 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 |

 git clone NVIDIA-DeepRecommender_-_2017-09-08_21-22-12.bundle -b master 

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