research-article

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Authors:

Yuyuan LiAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6190 - 6201

https://doi.org/10.1145/3581783.3613831

Published: 27 October 2023 Publication History

Abstract

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root cause of the UOF issue: the learning process between advantaged and disadvantaged users is unfair. To tackle this issue, we propose an In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a general framework that can be applied to any backbone recommendation model to achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the UCDS modeling stage and the in-processing training stage. In the In-UCDS modeling stage, for each disadvantaged user, we extract a constrained dominant set (a user cluster) containing some advantaged users that are similar to it. In the in-processing training stage, we move the representations of disadvantaged users closer to their corresponding cluster by calculating a fairness loss. By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously. Comprehensive experiments on three real-world datasets demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a fairer model with better overall recommendation performance.

References

[1]

Leulseged Tesfaye Alemu, Marcello Pelillo, and Mubarak Shah. 2019. Deep constrained dominant sets for person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9855--9864.

[2]

Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research, Vol. 50, 1 (2021), 3--44.

[3]

Reuben Binns. 2018. Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency. PMLR, 149--159.

[4]

Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2021. Interplay between upsampling and regularization for provider fairness in recommender systems. User Modeling and User-Adapted Interaction, Vol. 31, 3 (2021), 421--455.

Digital Library

[5]

Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning. PMLR, 715--724.

[6]

Robin Burke. 2017. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017).

[7]

Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018. Balanced neighborhoods for multi-sided fairness in recommendation. In Conference on fairness, accountability and transparency. PMLR, 202--214.

[8]

Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng, and Li Wang. 2022. Differential private knowledge transfer for privacy-preserving cross-domain recommendation. In Proceedings of the ACM Web Conference 2022. 1455--1465.

Digital Library

[9]

Xu Chen, Ya Zhang, Ivor Tsang, Yuangang Pan, and Jingchao Su. 2020. Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation. arXiv preprint arXiv:2009.06884 (2020).

[10]

Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017. Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining. 797--806.

Digital Library

[11]

Enyan Dai and Suhang Wang. 2020. Learning Fair Graph Neural Networks with Limited and Private Sensitive Attribute Information. arXiv preprint arXiv:2009.01454 (2020).

[12]

Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2022. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. arXiv preprint arXiv:2204.08570 (2022).

[13]

Brian d'Alessandro, Cathy O'Neil, and Tom LaGatta. 2017. Conscientious classification: A data scientist's guide to discrimination-aware classification. Big data, Vol. 5, 2 (2017), 120--134.

[14]

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, and Krishna P Gummadi. 2021. When the umpire is also a player: Bias in private label product recommendations on e-commerce marketplaces. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 873--884.

Digital Library

[15]

Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2021a. A flexible framework for evaluating user and item fairness in recommender systems. User Modeling and User-Adapted Interaction, Vol. 31, 3 (2021), 457--511.

Digital Library

[16]

Yashar Deldjoo, Alejandro Bellogin, and Tommaso Di Noia. 2021b. Explaining recommender systems fairness and accuracy through the lens of data characteristics. Information Processing & Management, Vol. 58, 5 (2021), 102662.

Digital Library

[17]

Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Difonzo, and Dario Zanzonelli. 2022. A Survey of Research on Fair Recommender Systems. arXiv preprint arXiv:2205.11127 (2022).

[18]

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference. 214--226.

Digital Library

[19]

Andres Ferraro. 2019. Music cold-start and long-tail recommendation: bias in deep representations. In Proceedings of the 13th ACM Conference on Recommender Systems. 586--590.

Digital Library

[20]

Alireza Gharahighehi, Celine Vens, and Konstantinos Pliakos. 2021. Fair multi-stakeholder news recommender system with hypergraph ranking. Information Processing & Management, Vol. 58, 5 (2021), 102663.

Digital Library

[21]

Sruthi Gorantla, Amit Deshpande, and Anand Louis. 2021. On the problem of underranking in group-fair ranking. In International Conference on Machine Learning. PMLR, 3777--3787.

[22]

Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller. 2016. The case for process fairness in learning: Feature selection for fair decision making. In NIPS symposium on machine learning and the law, Vol. 1. Barcelona, Spain, 11.

[23]

Zhongxuan Han, Xiaolin Zheng, Chaochao Chen, Wenjie Cheng, and Yang Yao. 2023. Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW '23). Association for Computing Machinery, New York, NY, USA, 449--459. https://doi.org/10.1145/3543507.3583402

Digital Library

[24]

Qianxiu Hao, Qianqian Xu, Zhiyong Yang, and Qingming Huang. 2021. Pareto optimality for fairness-constrained collaborative filtering. In Proceedings of the 29th ACM International Conference on Multimedia. 5619--5627.

Digital Library

[25]

Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Advances in neural information processing systems, Vol. 29 (2016).

Digital Library

[26]

F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19.

[27]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.

Digital Library

[28]

Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the 27th ACM international conference on information and knowledge management. 667--676.

Digital Library

[29]

Ben Hutchinson and Margaret Mitchell. 2019. 50 years of test (un) fairness: Lessons for machine learning. In Proceedings of the conference on fairness, accountability, and transparency. 49--58.

Digital Library

[30]

Jian Kang, Jingrui He, Ross Maciejewski, and Hanghang Tong. 2020. Inform: Individual fairness on graph mining. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 379--389.

Digital Library

[31]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[32]

Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. Advances in neural information processing systems, Vol. 30 (2017).

[33]

Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, and Jun Wang. 2023. Selective and Collaborative Influence Function for Efficient Recommendation Unlearning. arXiv preprint arXiv:2304.10199 (2023).

[34]

Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021. User-oriented fairness in recommendation. In Proceedings of the Web Conference 2021. 624--632.

Digital Library

[35]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.

Digital Library

[36]

Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiajie Su, Xinting Liao, Mengling Hu, and Yanchao Tan. 2023. Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW '23). Association for Computing Machinery, New York, NY, USA, 383--394. https://doi.org/10.1145/3543507.3583366

Digital Library

[37]

Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment, Vol. 10, 10 (2017), 1010--1021.

Digital Library

[38]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems. 17--24.

Digital Library

[39]

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), Vol. 54, 6 (2021), 1--35.

Digital Library

[40]

Alessandro B Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, and Markus Schedl. 2021. Investigating gender fairness of recommendation algorithms in the music domain. Information Processing & Management, Vol. 58, 5 (2021), 102666.

Digital Library

[41]

Andriy Mnih and Russ R Salakhutdinov. 2007. Probabilistic matrix factorization. Advances in neural information processing systems, Vol. 20 (2007).

[42]

Massimiliano Pavan and Marcello Pelillo. 2006. Dominant sets and pairwise clustering. IEEE transactions on pattern analysis and machine intelligence, Vol. 29, 1 (2006), 167--172.

[43]

Hossein A Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, and Mohammad Aliannejadi. 2022. Experiments on Generalizability of User-Oriented Fairness in Recommender Systems. arXiv preprint arXiv:2205.08289 (2022).

[44]

Dougal Shakespeare, Lorenzo Porcaro, Emilia Gómez, and Carlos Castillo. 2020. Exploring artist gender bias in music recommendation. arXiv preprint arXiv:2009.01715 (2020).

[45]

Wenlong Sun, Sami Khenissi, Olfa Nasraoui, and Patrick Shafto. 2019. Debiasing the human-recommender system feedback loop in collaborative filtering. In Companion Proceedings of The 2019 World Wide Web Conference. 645--651.

Digital Library

[46]

Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In 2018 ieee/acm international workshop on software fairness (fairware). IEEE, 1--7.

Digital Library

[47]

Chu Wang, Marcello Pelillo, and Kaleem Siddiqi. 2019b. Dominant set clustering and pooling for multi-view 3d object recognition. arXiv preprint arXiv:1906.01592 (2019).

[48]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019a. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 165--174.

Digital Library

[49]

Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, and Ed H. Chi. 2022. Distributionally-Robust Recommendations for Improving Worst-Case User Experience. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW '22). Association for Computing Machinery, New York, NY, USA, 3606--3610. https://doi.org/10.1145/3485447.3512255

Digital Library

[50]

Bruna Wundervald. 2021. Cluster-based quotas for fairness improvements in music recommendation systems. International Journal of Multimedia Information Retrieval, Vol. 10, 1 (2021), 25--32.

[51]

Eyasu Zemene and Marcello Pelillo. 2016. Interactive image segmentation using constrained dominant sets. In European Conference on Computer Vision. Springer, 278--294.

[52]

Eyasu Zemene Zemene, Leulseged Tesfaye Alemu, and Marcello Pelillo. 2018. Dominant sets for ?constrained" image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 41, 10 (2018), 2438--2451.

Digital Library

[53]

Xiaolin Zheng, Jiajie Su, Weiming Liu, and Chaochao Chen. 2022a. DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation. In Proceedings of the 30th ACM International Conference on Multimedia (Lisboa, Portugal) (MM '22). Association for Computing Machinery, New York, NY, USA, 471--481. https://doi.org/10.1145/3503161.3548072

Digital Library

[54]

Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, and Bing Han. 2022b. Heterogeneous Information Crossing on Graphs for Session-Based Recommender Systems. ACM Trans. Web (dec 2022). https://doi.org/10.1145/3572407 Just Accepted.

Digital Library

Cited By

Han ZChen CZheng XZhang LLi YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Hypergraph Convolutional Network for User-Oriented Fairness in Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657737(903-913)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657737

Index Terms

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
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Tao Mei
HiDream.ai, China
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Rita Cucchiara
University of Modena and Reggio Emilia, Italy
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Program Chairs:
Marco Bertini
University of Florence, Italy
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Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
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Pradeep K. Atrey
University at Albany, State University of New York, USA
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M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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The ?Ten Thousand Talents Program? of Zhejiang Province for Leading Experts
The National Natural Science Foundation of China

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Han ZChen CZheng XZhang LLi YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Hypergraph Convolutional Network for User-Oriented Fairness in Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657737(903-913)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657737

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