Installation

1. Clone this git repository and change directory to this repository:

   git clone https://github.com/chongminggao/EasyRL4Rec.git
   cd EasyRL4Rec/
   

2. A new conda environment is suggested.

   conda create --name easyrl4rec python=3.11 -y
   

3. Activate the newly created environment.

   conda activate easyrl4rec
   

4. Install the required modules from pip.

   sh install.sh
   

   Install the tianshou package from my forked version:

   cd src
   git clone https://github.com/yuyq18/tianshou.git
   cd ..
   

Download the data

1. Download the compressed dataset

   wget https://nas.chongminggao.top:4430/easyrl4rec/data.tar.gz
   

   or you can manually download it from this website: https://rec.ustc.edu.cn/share/a3bdc320-d48e-11ee-8c50-4b1c32c31e9c

1. Uncompress the downloaded data.tar.gz. The following command will directly extract data.tar.gz into the .data/ directory and merge it with the existing files under .data/.

   tar -zxvf data.tar.gz
   

   Please note that the decompressed file size is as high as 8.1GB. This is due to the large space occupied by the ground-truth of the user-item interaction matrix.

If things go well, you can run the following examples now！Or you can just reproduce the results in the paper.