Description

The source code is for paper Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback.

Package Introduction

• datasets: the code for config and dataset class.
• models: the code for neural network in Pytorch.
• prepocess: the code for processing dataset.
• utils: the code for evalution and other tools.
• data: created by download.sh, containing all runtime files.

Prepare Data

First download the dataset.

chmod -R 700 download.sh
./download.sh

Afer that, the complete package structure is:

|-- datasets
|-- prepocess
|-- models
|-- utils
`-- data
    |-- train
        |-- behaviors.tsv
        |-- news.tsv
    |-- dev
        |-- behaviors.tsv
        |-- news.tsv
    |-- raw 
    `-- processed

Set Environments

We first need to create a python=3.7 virtualenv and activate it.

Then, we should intall some dependencies.

pip install -r requirements.txt

Our pytorch version is 1.6.0 and torch-geometric version is 1.6.1. If there is problem on building torch-sparse or torch-scatter, go for this webpage to download whl file directly. They are two package reqiured for using torch-geometric.

prepare the runnable dataset

The dataset downloaded from website cannot be used currently, and need to be processed first.

chmod -R 700 run.sh
./run.sh

run.sh assumes 4 GPUs will be used, if the GPU number is different, change the processes argument in the code:

python -m prepocess.convert_train --processes=#GPUs

Number of GPUs can be 1, 2, 4, 8.

Training and testing

To train the model, run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python training.py --gpus=4 --epoch=10

Based on the validation results, choose parameters from one epoch (i.e. epoch 3) to do the test:

CUDA_VISIBLE_DEVICES=0,1,2,3 python validate.py --gpus=4 --epoch=3 --filenum=20

In these instructions, argument gpus means the number of GPUs.

Experiment Environment

This model was trained and tested by using 4 Tesla P40 GPUs. The memory of the device was 128GB.