ProxyRCA

Implementation of the paper "Proxy-based Item Representation for Attribute and Context-aware Recommendation", accepted in The 17th ACM International Conference on Web Search and Data Mining (WSDM 2024) [arXiv link].

• raw dataset directory: ./raw/
  • put CARCA/Data as ./raw/CARCA/
  • ./raw/ml1m
  • ./raw/ml20m
• data directory: ./data/

For preprocessing, anaconda environment with requirements.txt installed is recommended.

python preprocess.py prepare --dname ml1m
python preprocess.py prepare --dname ml20m
python preprocess.py prepare --dname fashion
python preprocess.py prepare --dname beauty
python preprocess.py prepare --dname men
python preprocess.py prepare --dname game

python preprocess.py split_quarters --dname fashion

python preprocess.py count_stats

For GPU runs, first build docker:

./scripts/build.sh

Then, use the following (dockerized python run):

runpy () {
    docker run \
        -it \
        --rm \
        --init \
        --gpus '"device=0"' \
        --shm-size 32G \
        --volume="$HOME/.cache/torch:/root/.cache/torch" \
        --volume="$PWD:/workspace" \
        proxyrca \
        python "$@"
}

runpy entry.py fashion/proxyrca

Easy tensorboard:

./scripts/tboard.sh