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Average User-Side Counterfactual Fairness for Collaborative Filtering

Authors:

Meng WangAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 5

Article No.: 140, Pages 1 - 26

https://doi.org/10.1145/3656639

Published: 13 May 2024 Publication History

Abstract

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub-user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea is to achieve model fairness from a causal perspective. The remaining challenge is that we have no access to interventions, i.e., the counterfactual world that produces recommendations when each user has changed the sensitive attribute value. To this end, we first borrow the Rubin-Neyman potential outcome framework to define average causal effects of sensitive attributes. Next, we show that removing causal effects of sensitive attributes is equal to average counterfactual fairness in CF. Then, we use the propensity re-weighting paradigm to estimate the average causal effects of sensitive attributes and formulate the estimated causal effects as an additional regularization term. To the best of our knowledge, we are one of the first few attempts to achieve counterfactual fairness from the causal effect estimation perspective in CF, which frees us from building sophisticated causal graphs. Finally, experiments on three real-world datasets show the superiority of our proposed model.

References

[1]

Abdallatif Abu-Issa, Hala Butmeh, and Iyad Tumar. 2024. Modeling students’ preferences and knowledge for improving educational achievements. Frontiers in Computer Science 6 (2024), 1359770.

[2]

Enrique Amigó, Yashar Deldjoo, Stefano Mizzaro, and Alejandro Bellogín. 2023. A unifying and general account of fairness measurement in recommender systems. Information Processing & Management 60, 1 (2023), 103115.

Digital Library

[3]

Yahav Bechavod, Christopher Jung, and Steven Z. Wu. 2020. Metric-free individual fairness in online learning. In NIPS, Vol. 33.

[4]

Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7, 11 (2006).

[5]

Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow. 2019. Fairness in recommendation ranking through pairwise comparisons. In SIGKDD. 2212–2220.

[6]

Avishek Joey Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In ICML. 715–724.

[7]

Jason Brownlee. 2020. Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-sensitive Learning. Machine Learning Mastery.

[8]

Desheng Cai, Shengsheng Qian, Quan Fang, Jun Hu, and Changsheng Xu. 2022. User cold-start recommendation via inductive heterogeneous graph neural network. ACM Transactions on Information Systems (2022).

[9]

Òscar Celma Herrada. 2009. Music Recommendation and Discovery in the Long Tail. Universitat Pompeu Fabra.

[10]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems (2020).

[11]

Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023. Improving recommendation fairness via data augmentation. In Proceedings of the ACM Web Conference 2023. 1012–1020.

Digital Library

[12]

Wei Chen, Yiqing Wu, Zhao Zhang, Fuzhen Zhuang, Zhongshi He, Ruobing Xie, and Feng Xia. 2023. FairGap: Fairness-aware recommendation via generating counterfactual graph. ACM Transactions on Information Systems (2023).

[13]

Silvia Chiappa. 2019. Path-specific counterfactual fairness. In AAAI. 7801–7808.

Digital Library

[14]

Enyan Dai and Suhang Wang. 2021. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In WSDM. 680–688.

Digital Library

[15]

Leander De Schutter and David De Cremer. 2023. How counterfactual fairness modelling in algorithms can promote ethical decision-making. International Journal of Human–Computer Interaction (2023), 1–12.

[16]

Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Difonzo, and Dario Zanzonelli. 2023. Fairness in recommender systems: Research landscape and future directions. User Modeling and User-Adapted Interaction (2023), 1–50.

[17]

Leyan Deng, Defu Lian, Chenwang Wu, and Enhong Chen. 2022. Graph convolution network based recommender systems: Learning guarantee and item mixture powered strategy. Advances in Neural Information Processing Systems 35 (2022), 3900–3912.

[18]

Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, and Yongdong Zhang. 2022. Causal incremental graph convolution for recommender system retraining. IEEE Transactions on Neural Networks and Learning Systems (2022).

[19]

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in information access systems. Foundations and Trends® in Information Retrieval 16, 1-2 (2022), 1–177.

Digital Library

[20]

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring author gender in book rating and recommendation. In RecSys. 242–250.

Digital Library

[21]

Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, and Marie Davidian. 2011. Doubly robust estimation of causal effects. American Journal of Epidemiology 173, 7 (2011), 761–767.

[22]

Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. In NIPS. 3315–3323.

Digital Library

[23]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. TIIS 5, 4 (2015), 1–19.

Digital Library

[24]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639–648.

Digital Library

[25]

Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, and Yongdong Zhang. 2022. Addressing confounding feature issue for causal recommendation. ACM Transactions on Information Systems (2022).

Digital Library

[26]

Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.

[27]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[28]

Michal Kosinski, David Stillwell, and Thore Graepel. 2013. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences 110, 15 (2013), 5802–5805.

[29]

Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. 2019. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences 116, 10 (2019), 4156–4165.

[30]

Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. In NIPS. 4069–4079.

[31]

Preethi Lahoti, Krishna P. Gummadi, and Gerhard Weikum. 2019. iFair: Learning individually fair data representations for algorithmic decision making. In ICDE. 1334–1345.

[32]

Anja Lambrecht and Catherine Tucker. 2019. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science 65, 7 (2019), 2966–2981.

Digital Library

[33]

Chen Lei, Wu Le, Hong Richang, Zhang Kun, and Wang Meng. 2020. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In AAAI. 27–34.

[34]

Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021. Towards personalized fairness based on causal notion. In SIGIR. 1054–1063.

Digital Library

[35]

Dawen Liang, Laurent Charlin, and David M. Blei. 2016. Causal inference for recommendation. In Workshop at UAI.

[36]

Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2020. A general knowledge distillation framework for counterfactual recommendation via uniform data. In SIGIR. 831–840.

Digital Library

[37]

Mengfan Liu, Pengyang Shao, and Kun Zhang. 2021. Graph-based exercise- and knowledge-aware learning network for student performance prediction. In Artificial Intelligence: First CAAI International Conference, CICAI 2021, Hangzhou, China, June 5–6, 2021, Proceedings, Part I 1. Springer, 27–38.

Digital Library

[38]

Ou Lydia Liu and Mark Wilson. 2009. Gender differences in large-scale math assessments: PISA trend 2000 and 2003. Applied Measurement in Education 22, 2 (2009), 164–184.

[39]

Orestis Loukas and Ho-Ryun Chung. 2023. Demographic parity: Mitigating biases in real-world data. arXiv preprint arXiv:2309.17347 (2023).

[40]

Ting Ma, Longtao Huang, Qianqian Lu, and Songlin Hu. 2022. KR-GCN: Knowledge-aware reasoning with graph convolution network for explainable recommendation. ACM Transactions on Information Systems (2022).

[41]

Mary Madden, Amanda Lenhart, Sandra Cortesi, Urs Gasser, Maeve Duggan, Aaron Smith, and Meredith Beaton. 2013. Teens, social media, and privacy. Pew Research Center 21, 1055 (2013), 2–86.

[42]

David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018. Learning adversarially fair and transferable representations. In ICML. 3381–3390.

[43]

Christos Andreas Makridis, Anthony Boese, Rafael Fricks, Don Workman, Molly Klote, Joshua Mueller, Isabel J. Hildebrandt, Michael Kim, and Gil Alterovitz. 2023. Informing the ethical review of human subjects research utilizing artificial intelligence. Frontiers in Computer Science 5 (2023), 1235226.

[44]

Sushmita Mitra and Sankar K. Pal. 1995. Fuzzy multi-layer perceptron, inferencing and rule generation. IEEE Transactions on Neural Networks 6, 1 (1995), 51–63.

Digital Library

[45]

Andriy Mnih and Russ R. Salakhutdinov. 2007. Probabilistic matrix factorization. In NIPS, Vol. 20.

Digital Library

[46]

Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun. 2020. Two simple ways to learn individual fairness metrics from data. In International Conference on Machine Learning. PMLR, 7097–7107.

[47]

Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. John Wiley & Sons.

[48]

Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. 2008. Discrimination-aware data mining. In SIGKDD. 560–568.

Digital Library

[49]

Felix Petersen, Debarghya Mukherjee, Yuekai Sun, and Mikhail Yurochkin. 2021. Post-processing for individual fairness. Advances in Neural Information Processing Systems 34 (2021), 25944–25955.

[50]

Christine Pinney, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 2023. Much Ado about gender: Current practices and future recommendations for appropriate gender-aware information access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. 269–279.

Digital Library

[51]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452–461.

Digital Library

[52]

James M. Robins, Andrea Rotnitzky, and Lue Ping Zhao. 1994. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 427 (1994), 846–866.

[53]

Lucas Rosenblatt and R. Teal Witter. 2023. Counterfactual fairness is basically demographic parity. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 14461–14469.

Digital Library

[54]

Donald B. Rubin. 2005. Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. 100, 469 (2005), 322–331.

[55]

Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased recommender learning from missing-not-at-random implicit feedback. In WSDM. 501–509.

Digital Library

[56]

Masahiro Sato, Sho Takemori, Janmajay Singh, and Tomoko Ohkuma. 2020. Unbiased learning for the causal effect of recommendation. In RecSys. 378–387.

Digital Library

[57]

Jasjeet S. Sekhon. 2008. The Neyman-Rubin model of causal inference and estimation via matching methods. The Oxford Handbook of Political Methodology 2 (2008), 1–32.

[58]

Pengyang Shao, Le Wu, Lei Chen, Kun Zhang, and Meng Wang. 2022. FairCF: Fairness-aware collaborative filtering. Science China Information Sciences 65, 12 (2022), 1–15.

[59]

Jie Shuai, Le Wu, Kun Zhang, Peijie Sun, Richang Hong, and Meng Wang. 2023. Topic-enhanced graph neural networks for extraction-based explainable recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1188–1197.

Digital Library

[60]

Latanya Sweeney. 2013. Discrimination in online ad delivery. Commun. ACM 56, 5 (2013), 44–54.

Digital Library

[61]

Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, and Shijin Wang. 2020. Neural cognitive diagnosis for intelligent education systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 6153–6161.

[62]

Fei Wang and Changshui Zhang. 2006. Label propagation through linear neighborhoods. In ICML. 985–992.

Digital Library

[63]

Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2021. Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In SIGIR. 1288–1297.

Digital Library

[64]

Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016. Learning to rank with selection bias in personal search. In SIGIR. 115–124.

Digital Library

[65]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165–174.

Digital Library

[66]

Yifan Wang, Weizhi Ma, Min Zhang*, Yiqun Liu, and Shaoping Ma. 2022. A survey on the fairness of recommender systems. ACM Journal of the ACM (JACM) (2022).

[67]

Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021. Fairness-aware news recommendation with decomposed adversarial learning. In AAAI. 4462–4469.

[68]

Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, and Xue Liu. 2022. A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation. ACM Transactions on Information Systems 41, 2 (2022), 1–29.

Digital Library

[69]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726–735.

Digital Library

[70]

Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang. 2021. Learning fair representations for recommendation: A graph-based perspective. In WWW. 2198–2208.

[71]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering (2022).

Digital Library

[72]

Yongkai Wu, Lu Zhang, and Xintao Wu. 2019. Counterfactual fairness: Unidentification, bound and algorithm. In IJCAI. 1438–1444.

[73]

Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, and Zhaochun Ren. 2022. On the user behavior leakage from recommender system exposure. ACM Transactions on Information Systems (2022).

Digital Library

[74]

Yonghui Yang, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2021. Enhanced graph learning for collaborative filtering via mutual information maximization. In SIGIR. 71–80.

Digital Library

[75]

Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023. Generative-contrastive graph learning for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1117–1126.

Digital Library

[76]

Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. In NIPS. 2921–2930.

[77]

Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NIPS 33 (2020), 5812–5823.

[78]

Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013. Learning fair representations. In ICML. 325–333.

[79]

Chen Zhao, Le Wu, Pengyang Shao, Kun Zhang, Richang Hong, and Meng Wang. 2023. Fair representation learning for recommendation: A mutual information perspective. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4911–4919.

Digital Library

[80]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In SIGKDD. 1059–1068.

Digital Library

Cited By

Zhang DZhang KWu LTian MHong RWang MBaeza-Yates RBonchi F(2024)Path-Specific Causal Reasoning for Fairness-aware Cognitive DiagnosisProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672049(4143-4154)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672049
Cai MChen LWang YBai HSun PWu LZhang MWang MBaeza-Yates RBonchi F(2024)Popularity-Aware Alignment and Contrast for Mitigating Popularity BiasProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671824(187-198)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671824
Li JWu LDu YHong RLi W(2024)Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking ServicesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340938311:5(7020-7031)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3409383

Index Terms

Average User-Side Counterfactual Fairness for Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 5

September 2024

809 pages

EISSN:1558-2868

DOI:10.1145/3618083

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 13 May 2024

Online AM: 11 April 2024

Accepted: 29 March 2024

Revised: 27 December 2023

Received: 15 February 2023

Published in TOIS Volume 42, Issue 5

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National Key Research and Development Program of China
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Cited By

Zhang DZhang KWu LTian MHong RWang MBaeza-Yates RBonchi F(2024)Path-Specific Causal Reasoning for Fairness-aware Cognitive DiagnosisProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672049(4143-4154)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672049
Cai MChen LWang YBai HSun PWu LZhang MWang MBaeza-Yates RBonchi F(2024)Popularity-Aware Alignment and Contrast for Mitigating Popularity BiasProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671824(187-198)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671824
Li JWu LDu YHong RLi W(2024)Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking ServicesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340938311:5(7020-7031)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3409383

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