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A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

Authors:

Masoud Mansoury,

Himan Abdollahpouri,

Mykola Pechenizkiy,

Bamshad Mobasher,

Robin BurkeAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 32, Pages 1 - 31

https://doi.org/10.1145/3470948

Published: 16 November 2021 Publication History

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Abstract

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

References

[1]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction 30, 1 (2020), 127–158.

Digital Library

[2]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning to rank recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, New York, NY, 42–46.

Digital Library

[3]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In Proceedings of the 32nd International Flairs Conference.

[4]

Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. In Proceedings of the KDD Workshop on Industrial Recommendation Systems.

[5]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2020. Addressing the multistakeholder impact of popularity bias in recommendation through calibration. arXiv:2007.12230.

[6]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On over-specialization and concentration bias of recommendations: Probabilistic neighborhood selection in collaborative filtering systems. In Proceedings of the 8th ACM Conference on Recommender Systems. 153–160.

Digital Library

[7]

Gediminas Adomavicius and YoungOk Kwon. 2011. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24, 5 (2011), 896–911.

Digital Library

[8]

Gediminas Adomavicius and YoungOk Kwon. 2011. Maximizing aggregate recommendation diversity: A graph-theoretic approach. In Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS’11). 3–10.

[9]

Arda Antikacioglu and R. Ravi. 2017. Post processing recommender systems for diversity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 707–716.

Digital Library

[10]

Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018. Equity of attention: Amortizing individual fairness in rankings. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 405–414.

Digital Library

[11]

Pablo Castells, Neil J. Hurley, and Saul Vargas. 2015. Novelty and diversity in recommender systems. In Recommender Systems Handbook. Springer, 881–918.

[12]

Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM Conference on Recommender Systems. 224–232.

Digital Library

[13]

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. arXiv:2010.03240.

[14]

Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, and Yoni Halpern. 2020. Fairness is not static: Deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 525–534.

Digital Library

[15]

Efim A. Dinic. 1970. Algorithm for solution of a problem of maximum flow in networks with power estimation. Soviet Mathematics Doklady 11 (1970), 1277–1280.

[16]

Lester Randolph Ford and Delbert R. Fulkerson. 1956. Maximal flow through a network. Canadian Journal of Mathematics 8 (1956), 399–404.

[17]

David García-Soriano and Francesco Bonchi. 2020. Fair-by-design matching. Data Mining and Knowledge Discovery 34 (2020), 1291–1335.

Digital Library

[18]

Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019. Fairness-aware ranking in search and recommendation systems with application to LinkedIn talent search. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2221–2231.

Digital Library

[19]

Andrew V. Goldberg and Robert E. Tarjan. 1988. A new approach to the maximum-flow problem. Journal of the ACM 35, 4 (1988), 921–940.

Digital Library

[20]

Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. LibRec: A Java library for recommender systems. In Proceedings of the UMAP Workshops.

[21]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1–19.

Digital Library

[22]

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

[23]

Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems 7, 1 (2016), 1–42.

Digital Library

[24]

Alexandros Karatzoglou, Linas Baltrunas, and Yue Shi. 2013. Learning to rank for recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems. 493–494.

Digital Library

[25]

Sami Khenissi. 2019. Modeling and Counteracting Exposure Bias in Recommender Systems. Master’s Thesis. University of Louisville.

[26]

Dominik Kowald, Markus Schedl, and Elisabeth Lex. 2020. The unfairness of popularity bias in music recommendation: A reproducibility study. In Proceedings of the European Conference on Information Retrieval. 35–42.

Digital Library

[27]

Haifeng Liu, Xiaomei Bai, Zhuo Yang, Amr Tolba, and Feng Xia. 2015. Trust-aware recommendation for improving aggregate diversity. New Review of Hypermedia and Multimedia 21, 3–4 (2015), 242–258.

Digital Library

[28]

Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, and Pheng Ann Heng. 2020. Balancing between accuracy and fairness for interactive recommendation with reinforcement learning. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 155–167.

[29]

Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020. FairMatch: A graph-based approach for improving aggregate diversity in recommender systems. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP’20). ACM, New York, NY, 154–162.

Digital Library

[30]

Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020. Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2145–2148.

Digital Library

[31]

Masoud Mansoury and Robin Burke. 2019. Algorithm selection with librec-auto. In Proceedings of AMIR@ECIR. 11–17.

[32]

Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness and satisfaction in recommendation systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2243–2251.

Digital Library

[33]

Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es. 2018. Do not blame it on the algorithm: An empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society 21, 7 (2018), 959–977.

[34]

Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020. FairRec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the Web Conference 2020. 1194–1204.

Digital Library

[35]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452–461.

Digital Library

[36]

Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. 175–186.

Digital Library

[37]

Robert Sanders. 1987. The Pareto principle: Its use and abuse. Journal of Services Marketing 1, 2 (1987), 37–40.

[38]

Rodrygo L. T. Santos, Craig Macdonald, and Iadh Ounis. 2010. Exploiting query reformulations for web search result diversification. In Proceedings of the 19th International Conference on World Wide Web. 881–890.

Digital Library

[39]

Markus Schedl. 2016. The LFM-1b dataset for music retrieval and recommendation. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. 103–110.

Digital Library

[40]

Ashudeep Singh and Thorsten Joachims. 2018. Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2219–2228.

Digital Library

[41]

Ayan Sinha, David F. Gleich, and Karthik Ramani. 2016. Deconvolving feedback loops in recommender systems. In Advances in Neural Information Processing Systems. 3243–3251.

Digital Library

[42]

Harald Steck. 2011. Item popularity and recommendation accuracy. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). 125–132.

Digital Library

[43]

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

[44]

Saúl Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems. 109–116.

Digital Library

[45]

Saúl Vargas and Pablo Castells. 2014. Improving sales diversity by recommending users to items. In Proceedings of the 8th ACM Conference on Recommender Systems. 145–152.

Digital Library

[46]

Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 2925–2934.

Digital Library

[47]

Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012. Challenging the long tail recommendation. arXiv: 1205.6700.

[48]

Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017. Fa* ir: A fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1569–1578.

Digital Library

[49]

Özge Sürer, Robin Burke, and Edward C. Malthouse. 2018. Multistakeholder recommendation with provider constraints. In Proceedings of the 12th ACM Conference on Recommender Systems. 54–62.

Digital Library

Cited By

Li NKang BLijffijt JDe Bie T(2024)FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited ResourcesACM Transactions on Intelligent Systems and Technology10.1145/364389115:4(1-24)Online publication date: 3-Feb-2024
https://dl.acm.org/doi/10.1145/3643891
Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Huebner BKolb TNeidhardt J(2024)Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and MetricsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664897(337-346)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664897
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Index Terms

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity
    2. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 November 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 2

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Cited By

Li NKang BLijffijt JDe Bie T(2024)FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited ResourcesACM Transactions on Intelligent Systems and Technology10.1145/364389115:4(1-24)Online publication date: 3-Feb-2024
https://dl.acm.org/doi/10.1145/3643891
Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Huebner BKolb TNeidhardt J(2024)Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and MetricsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664897(337-346)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664897
Wang YSun PMa WZhang MZhang YJiang PMa SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Intersectional Two-sided Fairness in RecommendationProceedings of the ACM on Web Conference 202410.1145/3589334.3645518(3609-3620)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645518
Liao JZhou WLuo FWen J(2024)Enhancing Disentanglement of Popularity Bias for Recommendation With Triplet Contrastive LearningIEEE Transactions on Services Computing10.1109/TSC.2024.337892517:3(921-933)Online publication date: May-2024
https://doi.org/10.1109/TSC.2024.3378925
Shi XLiu QXie HBai YShang M(2024)Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender SystemsIEEE Transactions on Services Computing10.1109/TSC.2024.334963617:3(1029-1043)Online publication date: May-2024
https://doi.org/10.1109/TSC.2024.3349636
Mashayekhi YKang BLijffijt JDe Bie T(2024)Scalable Job Recommendation With Lower Congestion Using Optimal TransportIEEE Access10.1109/ACCESS.2024.339022912(55491-55505)Online publication date: 2024
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Saifudin IWidiyaningtyas T(2024)Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, DatasetsIEEE Access10.1109/ACCESS.2024.335927412(19827-19847)Online publication date: 2024
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Liao JYang MZhou WZhang HWen J(2024)Modeling item exposure and user satisfaction for debiased recommendation with causal inferenceInformation Sciences10.1016/j.ins.2024.120834676(120834)Online publication date: Aug-2024
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Lopes RAlves RLedent ASantos RKloft M(2024)Recommendations with minimum exposure guaranteesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121164236:COnline publication date: 1-Feb-2024
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