CPR

TensorFlow implementation of our paper "Cross Pairwise Ranking for Unbiased Item Recommendation" (WWW'22).

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{cpr22,
  title={Cross Pairwise Ranking for Unbiased Item Recommendation},
  author={Wan, Qi and He, Xiangnan and Wang, Xiang and Wu, Jiancan and Guo, Wei and Tang, Ruiming},
  booktitle={Proceedings of the ACM Web Conference 2022},
  pages={2370--2378},
  year={2022}
}

Requirements

• python=3.6
• tensorflow-gpu=1.15.5
• numpy
• scipy
• pandas
• Cython

And make sure GCC has been installed in your environment.

Datasets

The preprocessed data have already been placed in the data/ folder. See data_preprocess.py if you want to know how they were generated from original data.

Compiling

The samplers and evaluators in our codes are mainly implemented as extension modules by Cython & Cpp, which is much faster than Python implementation. Run the following command to compile all the extension modules.

python setup.py build_ext --inplace

You can safely ignore the warnings in the output of this command.

Reproducing results

Here we list some commands that reproduce the results presented in our paper.

CPR

The following commands can reproduce the results of CPR on 4 backbones (MF, LightGCN, NeuMF, NGCF) and 2 datasets (MovieLens, Netflix).

MF

(MF is equivalent to 0-layer LightGCN.)

python CPR.py --dataset movielens_10m --lr 0.0001 --reg 0.001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --sample_rate 3 --sample_ratio 3 --eval_types valid test --eval_epoch 4 --early_stop 10 

python CPR.py --dataset netflix --lr 0.0001 --reg 0.001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --sample_rate 3 --sample_ratio 2 --eval_types valid test --eval_epoch 4 --early_stop 10 

LightGCN

python CPR.py --dataset movielens_10m --lr 0.001 --reg 0.01 --ks 20 --batch_size 2048 --n_layer 3 --embed_type lightgcn --sample_rate 8 --sample_ratio 7 --eval_types valid test --eval_epoch 1 --early_stop 10 

python CPR.py --dataset netflix --lr 0.001 --reg 0.0001 --ks 20 --batch_size 2048 --n_layer 3 --embed_type lightgcn --sample_rate 7 --sample_ratio 4 --eval_types valid test --eval_epoch 1 --early_stop 10 

NeuMF

python CPR.py --dataset movielens_10m --lr 0.001 --reg 0.0001 --weight_reg 0.01 --weight_sizes 256 --ks 20 --batch_size 2048 --eval_batch_size 16 --n_layer 0 --embed_type lightgcn --inference_type mlp --sample_rate 1 --sample_ratio 4 --eval_types valid test --eval_epoch 10 --early_stop 5 

python CPR.py --dataset netflix --lr 0.001 --reg 0.0001 --weight_reg 0.01 --weight_sizes 256 --ks 20 --batch_size 2048 --eval_batch_size 16 --n_layer 0 --embed_type lightgcn --inference_type mlp --sample_rate 1 --sample_ratio 6 --eval_types valid test --eval_epoch 10 --early_stop 5 

NGCF

python CPR.py --dataset movielens_10m --lr 0.001 --reg 0.01 --weight_reg 0 --ks 20 --batch_size 2048 --n_layer 3 --embed_type ngcf --sample_rate 1.5 --sample_ratio 5 --eval_types valid test --eval_epoch 1 --early_stop 10 

python CPR.py --dataset netflix --lr 0.0001 --reg 0.001 --weight_reg 0.01 --ks 20 --batch_size 2048 --n_layer 3 --embed_type ngcf --sample_rate 3 --sample_ratio 4 --eval_types valid test --eval_epoch 4 --early_stop 10 

Baselines

We also implement some of the baselines. The following commands can reproduce their results on MF.

BPR

python BPR.py --dataset movielens_10m --lr 0.0001 --reg 0 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --eval_types valid test --eval_epoch 4 --early_stop 10 

python BPR.py --dataset netflix --lr 0.0001 --reg 0.00001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --eval_types valid test --eval_epoch 4 --early_stop 10 

UBPR

python UBPR.py --dataset movielens_10m --lr 0.0001 --reg 0.001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --ps_pow 0.8 --clip 0 --eval_types valid test --eval_epoch 4 --early_stop 10 

python UBPR.py --dataset netflix --lr 0.0001 --reg 0.0001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --ps_pow 0.7 --clip 0 --eval_types valid test --eval_epoch 4 --early_stop 10 

DICE

python DICE.py --dataset movielens_10m --lr 0.0001 --reg 0.01 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --int_weight 9 --pop_weight 9 --dis_pen 0.0001 --margin 10 --margin_decay 0.9 --loss_decay 0.9 --eval_types valid test --eval_epoch 4 --early_stop 10 

python DICE.py --dataset netflix --lr 0.0001 --reg 0.01 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --int_weight 9 --pop_weight 9 --dis_pen 0.0001 --margin 40 --margin_decay 0.9 --loss_decay 0.9 --eval_types valid test --eval_epoch 4 --early_stop 10 

Output

Here is an example of the output log. It is the output of command:

python CPR.py --dataset movielens_10m --lr 0.0001 --reg 0.001 --ks 20 --batch_size 2048 --n_layer 0 --embed_type lightgcn --sample_rate 3 --sample_ratio 3 --eval_types valid test --eval_epoch 4 --early_stop 10 

...
Epoch 1 :    3.51854 s | loss = 0.69320 = 0.69319 + 0.00000
Epoch 2 :    2.98906 s | loss = 0.69284 = 0.69283 + 0.00000
Epoch 3 :    2.98795 s | loss = 0.69080 = 0.69079 + 0.00001
Epoch 4 :    3.20985 s | loss = 0.68585 = 0.68583 + 0.00002
============================================================================================================================================
[ valid set ]
---- Item ----
Recall    @20 :   0.16620
Precision @20 :   0.02216
NDCG      @20 :   0.08712
Rec       @20 :  20.00000
ARP       @20 :1530.23474
[ test set ]
---- Item ----
Recall    @20 :   0.17162
Precision @20 :   0.03469
NDCG      @20 :   0.10235
ARP       @20 :1588.61609
Evaluation :    1.39915 s
============================================================================================================================================
...
Early stopping triggered.
Best epoch: 76.
[ test set ]
---- Item ----
Recall    @20 :   0.20007
Precision @20 :   0.04061
NDCG      @20 :   0.12209
ARP       @20 :1071.91736
============================================================================================================================================