CPR: Cross-domain Preference Ranking with User Transformation
Our codes for https://dl.acm.org/doi/abs/10.1007/978-3-031-28238-6_35
0. Environment
- Docker image:
nvcr.io/nvidia/pytorch:22.05-py3
pip install -r requirements.txt
1. Data
- Enter preprocess directory
cd preprocess/
- Download
raw
data
bash download_amazon_data.sh {raw_data_dir}
- Process
raw
toloo
(It takes long time, particularlyBooks_5
)
bash raw_to_loo.sh {raw_data_dir} {loo_data_dir}
- Process
loo
toloo-5core
bash loo_to_ncore.sh {loo_data_dir} {ncore_data_dir}
- Generate
input
for CPR
bash generate_cpr_input.sh {ncore_data_dir} {cpr_input_dir}
2. Model Training & Evaluation
- Enter CPR directory
$ cd models/CPR
- Compile cpp code
make
- Usage example
bash run_cpr.sh\
{ncore_data_dir}\
{cpr_input_dir}\
{emb_save_dir}\
{exp_record_dir}\
traineval\
hk\
csjj