KG4RecEval

Official implement of paper "Does Knowledge Graph Really Matter for Recommender Systems?"

Requirements

Tested on python 3.9 and Ubuntu 20.04.

1. pytorch==2.0.0
2. recbole==1.1.1
3. lightgbm
4. xgboost
5. ray
6. thop
7. torch_scatter
8. hyperopt
9. dgl==0.9.1

Dataset process

Please refer to recbole to download the datasets and our paper for details.

Tips

1. Our results are saved in ./result. If you want to run experiments, please move ./result to other place or just delete it.
2. Replace np.float with float in recbole.evaluator.metrics as there is a conflict between recbole and high version of numpy.
3. Run python main.py -h to see the usage.

No knowledge experiment example

python main.py --experiment noknowledge --dataset lastfm --worker_num 1 --eval_times 5 --model KGAT KGCN RippleNet CFKG CKE KGNNLS KTUP KGIN --test_type_list fact --rate 1 --save_dataset --save_dataloaders

False experiment example

python main.py --experiment false --dataset lastfm --worker_num 1 --rate 0 0.25 0.5 0.75 1.0 --eval_times 5 --model KGAT KGCN RippleNet CFKG CKE KGNNLS KTUP KGIN --test_type_list kg --save_dataset --save_dataloaders

Decrease experiment example

python main.py --experiment decrease --dataset lastfm --worker_num 1 --eval_times 5 --model KGAT KGCN RippleNet CFKG CKE KGNNLS KTUP KGIN  --test_type_list fact entity relation --rate 0.25 0.5 0.75 1 --save_dataset --save_dataloaders

Cold-start experiment example

python main.py --experiment coldstart --dataset lastfm --worker_num 1 --eval_times 5 --model KGAT KGCN RippleNet CFKG CKE KGNNLS KTUP KGIN --test_user_ratio 0.1 --cs_threshold 3 --test_type_list random d_fact --rate 0 0.25 0.5 0.75 1 --save_dataset --save_dataloaders