Description

This code is for "Graph Neural News Recommendation with User Existing and Potential Interest Modeling".

Package Structure

• src: the source code
• conf: the configure files
• data: the dataset folder

Prepare Data

Since the dataset is very large scale and has been publiced by the MIND, you need to download it from the MIND website. After downloading, you will get three zip files MINDlarge_train.zip, MINDlarge_dev.zip and MINDlarge_test.zip.

Then, uncompress them and put their subfiles to the corresponding subfolders of data. For example, the files in MINDlarge_train.zip should be put at the "data/L/train"

Afer that, the complete package structure is:

|-- src
|-- conf
`-- data
    |-- L
        |-- train
            |-- behaviors.tsv
            |-- news.tsv
        |-- dev
            |-- behaviors.tsv
            |-- news.tsv
        |-- test 
            |-- behaviors.tsv
            |-- news.tsv
        |-- result
        `-- hop1_cocur_bip_hist50

Set Environments

We first need to create a python=3.6 virtualenv and activate it.

Then, we should intall some dependencies.

pip install -r requirements.txt

Next, we should set the system environment $MINDWD as the current path. For example,

export MINDWD=/home/root/KG-Recommender

Replace /home/root as the real parent directory path of KG-Recommender.

Source Code

Please read the src/README.md to get more details about the source code and the usages.

Citation

If you use this code, please cite the paper.

@article{Qiu2022GraphNN,
  title={Graph Neural News Recommendation with User Existing and Potential Interest Modeling},
  author={Zhaopeng Qiu and Yunfan Hu and Xian Wu},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  year={2022}
}