RecBole-FairRec

RecBole-FairRec is a library toolkit built upon RecBole for reproducing and developing fairness-aware recommendation.

Highlights

• Easy-to-use: Our library shares unified API and input(atomic files) as RecBole.
• Conveniently learn and compare: Our library provides several fairess-metrics and frameworks for learning and comparing.
• Extensive FairRec library: Recently proposed fairness-aware algorithms can be easily equipped in our library.

Requirements

torch>=1.7.0
numpy>=1.17.2
scipy==1.6.0
hyperopt>=0.2.4
pandas>=1.0.5
tqdm>=4.48.2
scikit_learn>=0.23.2
pyyaml>=5.1.0
colorlog==4.7.2
colorama==0.4.4
tensorboard>=2.5.0
scikit_learn>=1.0.0
tqdm>=4.0.0

Quick-Start

With the source code, you can use the provided script for initial usage of our library:

python run_recbole.py

If you want to change the models or datasets, just run the script by setting additional command parameters:

python run_recbole.py -m [model] -d [dataset] -c [config_files]

Implement Models

We list the models that we have implemented up to now:

• FOCF from Sirui Yao et al:Beyond Parity：Fairness Objectives for Collaborative Filtering(NIPS 2017). Note: We implement this model with ranking-based metrics, e.g. NDCG@K.
• PFCN from Yunqi Li et al:Towards Personalized Fairness based on Causal Notion(SIGIR 2021)
  • PFCN_MLP
  • PFCN_BiasedMF
  • PFCN_DMF
  • PFCN_PMF
• FairGo from Wu Le et al:Learning Fair Representations for Recommendation: A Graph-based Perspective (WWW 2021)
  • FairGo_PMF(WAP,LBA,LVA)
  • FairGo_GCN(WAP,LBA,LVA)
• NFCF from Rashidul Islam et al:Debiasing career recommendations with neural fair collaborative filtering (WWW 2021)

Datasets

The datasets used can be downloaded from Datasets Link.

LeaderBoard Results

	GiniIndex	PopularityPercentage		DifferentialFairness	ValueUnfairness	AbsoluteUnfairness	UnderUnfairness	OverUnfairness	NonParityUnfairness		NDCG@5	Recall@5	Hit@5	MRR@5
BPR	0.9873	0.9991		1.493	0.1308	0.1024	0.0406	0.0903	0.0189		0.263	0.1347	0.6518	0.4317
NCF	0.9353	0.884		1.4781	0.0824(1)	0.0548(1)	0.0439	0.0384	0.0101		0.4717	0.2538	0.8972	0.6762
PMF	0.9876	0.9766		1.5714	0.1293	0.1139	0.0203	0.109	0.0013(2)		0.2196	0.1209	0.5944	0.3717
GCN	0.9878	0.9291		1.5837	0.13	0.1177	0.0128	0.1172	0.01		0.1982	0.1045	0.554	0.3428
BiasedMF	0.9874	1		1.455	0.127	0.0926(3)	0.0318	0.0952	0.0149		0.2643	0.1381	0.6627	0.4322
DMF	0.9167(2)	0.8359(2)		1.4847	0.1055(3)	0.0948	0.0083(2)	0.0972	0.0068		0.4882(3)	0.2663(3)	0.9028(3)	0.7011
FOCF_value	0.9875	0.9973		1.1039(2)	0.1275	0.1051	0.1107	0.0168(2)	0.0204		0.2422	0.1299	0.6316	0.405
FOCF_abs	0.9871	0.9939		1.2727(3)	0.1287	0.1051	0.1045	0.0242(3)	0.0061		0.2407	0.129	0.6291	0.4034
FOCF_under	0.9797	0.902		1.4435	0.1287	0.104	0.0295	0.0992	0.0051		0.2375	0.1353	0.6247	0.4055
FOCF_over	0.9875	0.9977		1.0641(1)	0.1274	0.1055	0.1125	0.0149(1)	0.0297		0.2425	0.1301	0.6325	0.4053
FOCF_nonparity	0.9877	0.9999		1.2878	0.1267	0.0895(2)	0.071	0.0556	0.0004(1)		0.2569	0.1381	0.6594	0.4253
PFCN_MLP	0.9875	1		1.5539	0.1283	0.1277	0.0003(1)	0.128	0.0035		0.2621	0.1371	0.6578	0.4309
PFCN_BiasedMF	0.9874	1		1.4821	0.1283	0.0984	0.0395	0.0888	0.0071		0.2644	0.1384	0.6629	0.434
PFCN_DMF	0.9873	1		1.5357	0.1277	0.1071	0.0284	0.0993	0.0018(3)		0.2578	0.1351	0.654	0.422
PFCN_PMF	0.9873	1		1.4272	0.1261	0.096	0.0223	0.1038	0.0101		0.2608	0.1374	0.6642	0.4296
FairGo_PMF_WAP	0.9822	0.9283		1.5781	0.1321	0.1171	0.0166	0.1156	0.0057		0.2187	0.1283	0.5922	0.3691
FairGo_PMF_LVA	0.9822	0.9232		1.5824	0.1321	0.1198	0.0143	0.1179	0.007		0.2127	0.1199	0.5781	0.3651
FairGo_PMF_LBA	0.9821	0.9024		1.5842	0.1322	0.1203	0.0139	0.1183	0.0056		0.2109	0.1156	0.577	0.3624
FairGo_GCN_WAP	0.9881	0.9161		1.5783	0.1304	0.1156	0.0175	0.1128	0.0081		0.1699	0.0968	0.5051	0.3024
FairGo_GCN_LVA	0.9881	0.9819		1.5841	0.1304	0.118	0.0143	0.1161	0.0051		0.1939	0.1111	0.552	0.3252
FairGo_GCN_LBA	0.9881	0.9821		1.5841	0.1304	0.118	0.0143	0.1161	0.0051		0.1938	0.1111	0.5518	0.3251
NCF_MLP	0.9035(1)	0.8206(1)		1.5627	0.1054(2)	0.0942	0.009	0.0965	0.0043		0.5051(1)	0.2756(1)	0.9154(1)	0.7109
NFCF	0.9193(3)	0.8628(3)		1.5366	0.1151	0.0974	0.0133	0.1018	0.0045		0.4927(2)	0.2693(2)	0.9094(2)	0.7022

The Team

RecBole-FairRec is developed and maintained by Jiakai Tang (@Tangjiakai).

Acknowledgement

The implementation is based on the open-source recommendation library RecBole.

Please cite the following paper as the reference if you use our code or processed datasets.

@inproceedings{zhao2021recbole,
  title={Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms},
  author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Hui Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen},
  booktitle={{CIKM}},
  year={2021}
}