PKU-ICST-MIPL / Firzen_ICDE2024

Firzen: Firing Strict Cold-start Items with Heterogeneous and Homogeneous Graphs for Recommendation

This is the source code of our ICDE 2024 paper "Firzen: Firing Strict Cold-start Items with Heterogeneous and Homogeneous Graphs for Recommendation".

Installation

conda create -n Firzen python=3.8 anaconda
conda activate Firzen
# Please install PyTorch according to your CUDA version.
conda install pytorch==1.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

Prepare Datasets

Our model is evaluated on Amazon Beauty, Amazon Cell Phones and Accessories, Amazon Clothing Shoes and Jewelry datasets.

You can download our processed datasets and unzip them to your data folder:

• Baidu Yun Pan: 1207

Quick Start

# Amazon_Beauty
python run_itemcoldstart.py --dataset=Amazon_Beauty --model=Firzen --config_files=configs/knowledge_rec_beauty.yaml

# Amazon_Cell_Phones_and_Accessories
python run_itemcoldstart.py --dataset=Amazon_Cell_Phones_and_Accessories --model=Firzen --config_files=configs/knowledge_rec_cell_phones.yaml

# Amazon_Clothing_Shoes_and_Jewelry
python run_itemcoldstart.py --dataset=Amazon_Clothing_Shoes_and_Jewelry --model=Firzen --config_files=configs/knowledge_rec_clothing.yaml

Acknowledgement

Our code references the following projects. Many thanks to the authors.

• Recbole
• MMRec
• MMSSL