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MACR

This is an implemention for our SIGKDD 2021 paper based on tensorflow

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

by Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi and Xiangnan He.

Introduction

MACR is a general popularity debias framework based on causal inference and counterfactual reasoning.

Requirements

  • tensorflow == 1.14
  • Numpy == 1.16.0
  • python == 3.6
  • Cython == 0.29.24 (Optional)
  • CUDA v10

For LightGCN C++ evaluation, please install Cython and do

cd macr_lightgcn
python setup.py build_ext --inplace

Run the code

Like what we mentioned in our paper, great performance can be obtained by easily setting C=30 or C=40. Here we show the example results of setting C=40.

Normal MF :

Change the dataset argument to run experiments on different datasets

python ./macr_mf/train.py --dataset addressa --batch_size 1024 --cuda 0 --saveID 1 --log_interval 10 --lr 0.001 --train normalbce --test normal

MACR MF:

ML10M

python ./macr_mf/train.py --dataset ml_10m --batch_size 8192 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 40 --train rubibceboth --test rubi --alpha 1e-3 --beta 1e-3

Gowalla

python ./macr_mf/train.py --dataset gowalla --batch_size 4096 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 40 --train rubibceboth --test rubi --alpha 1e-2 --beta 1e-3

Globe

python ./macr_mf/train.py --dataset globe --batch_size 4096 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 40 --train rubibceboth --test rubi --alpha 1e-3 --beta 1e-3

Yelp2018

python ./macr_mf/train.py --dataset yelp2018 --batch_size 4096 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 40 --train rubibceboth --test rubi --alpha 1e-2 --beta 1e-3

Adressa

python ./macr_mf/train.py --dataset addressa --batch_size 1024 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 40 --train rubibceboth --test rubi --alpha 1e-3 --beta 1e-3

Normal LightGCN:

python macr_lightgcn/LightGCN.py --data_path data/ --dataset addressa --verbose 1 --layer_size [64,64] --Ks [20] --loss bce --test normal --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 1024 --gpu_id 1 --log_interval 10

MACR LightGCN:

ML10M

python macr_lightgcn/LightGCN.py --data_path data/ --dataset ml_10m --verbose 1 --layer_size [64,64] --Ks [20] --loss bceboth --test rubiboth --c 40 --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 8192 --gpu_id 0 --log_interval 10 --alpha 1e-2 --beta 1e-3

Gowalla

python macr_lightgcn/LightGCN.py --data_path data/ --dataset gowalla --verbose 1 --layer_size [64,64] --Ks [20] --loss bceboth --test rubiboth --c 40 --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 4096 --gpu_id 0 --log_interval 10 --alpha 1e-2 --beta 1e-3

Globe

python macr_lightgcn/LightGCN.py --data_path data/ --dataset globe --verbose 1 --layer_size [64,64] --Ks [20] --loss bceboth --test rubiboth --c 40 --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 4096 --gpu_id 0 --log_interval 10 --alpha 1e-2 --beta 1e-3

Yelp2018

python macr_lightgcn/LightGCN.py --data_path data/ --dataset yelp2018 --verbose 1 --layer_size [64,64] --Ks [20] --loss bceboth --test rubiboth --c 40 --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 4096 --gpu_id 0 --log_interval 10 --alpha 1e-2 --beta 1e-3

Adressa

python macr_lightgcn/LightGCN.py --data_path data/ --dataset addressa --verbose 1 --layer_size [64,64] --Ks [20] --loss bceboth --test rubiboth --c 40 --epoch 2000 --early_stop 1 --lr 0.001 --batch_size 1024 --gpu_id 0 --log_interval 10 --alpha 1e-2 --beta 1e-3
HR Rec NDCG
LightGCN_ML10M 0.15262 0.04745 0.02844
LightGCN_Gowalla 0.28410 0.09076 0.05999
LightGCN_Globe 0.12312 0.05271 0.02854
LightGCN_Adressa 0.16356 0.12967 0.06071
LightGCN_Yelp 0.17210 0.04016 0.02649
MF_ML10M 0.14011 0.04087 0.02407
MF_Gowalla 0.26669 0.08488 0.05756
Mf Globe 0.10725 0.04594 0.02513
MF Adressa 0.13561 0.10612 0.04667
MF Yelp 0.14444 0.02863 0.02039

Fixing C to 40 can get great performance. Tuning the value of C through validation (create your validation set) will get higher performance. For example on ML10M dataset:

# Finetune c
python ./macr_mf/tune.py --dataset ml_10m/val --batch_size 8192 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --start 30 --end 40 --step 11 --train rubibceboth --test rubi --alpha 1e-3 --beta 1e-3 --valid_set valid
# Run experiment
python ./macr_mf/train.py --dataset ml_10m --batch_size 8192 --cuda 0 --saveID 0 --log_interval 10 --lr 0.001 --check_c 1 --c 32 --train rubibceboth --test rubi --alpha 1e-3 --beta 1e-3 --valid_set test
HR Rec NDCG
MF_ML10M (C=40) 0.14011 0.04087 0.02407
MF_ML10M (Validation C=32) 0.14545 0.04235 0.02518

Acknowledgement

Very thanks for Chufeng Shi for his help on code and the LightGCN code repo.

Citation

If you find this paper helpful, please cite our paper.

@inproceedings{wei2021model,
  title={Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System},
  author={Wei, Tianxin and Feng, Fuli and Chen, Jiawei and Wu, Ziwei and Yi, Jinfeng and He, Xiangnan},
  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  pages={1791--1800},
  year={2021}
}