NVIDIA/DeepRecommender

Name: DeepRecommender

Owner: NVIDIA Corporation

Description: Deep learning for recommender systems

Created: 2017-09-08 20:48:44.0

Updated: 2018-04-03 00:41:33.0

Pushed: 2018-03-27 16:41:51.0

Homepage: null

Size: 1382

Language: Python

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README

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 >= 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.

thon -m unittest test/data_layer_tests.py
thon -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

  r -xvf nf_prize_dataset.tar.gz
  r -xf download/training_set.tar
  thon ./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.

on run.py --gpu_ids 0 \
th_to_train_data Netflix/NF_TRAIN \
th_to_eval_data Netflix/NF_VALID \
dden_layers 512,512,1024 \
n_linearity_type selu \
tch_size 128 \
gdir model_save \
op_prob 0.8 \
timizer momentum \
 0.005 \
ight_decay 0 \
g_step 1 \
ise_prob 0 \
m_epochs 12 \
mmary_frequency 1000

Note that you can run Tensorboard in parallel

nsorboard --logdir=model_save

Run inference on the Test set

on infer.py \
th_to_train_data Netflix/NF_TRAIN \
th_to_eval_data Netflix/NF_TEST \
dden_layers 512,512,1024 \
n_linearity_type selu \
ve_path model_save/model.epoch_11 \
op_prob 0.8 \
edictions_path preds.txt

Compute Test RMSE

on 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 |