skip to main content
10.1145/3640457.3688169acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
short-paper

Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation

Published: 08 October 2024 Publication History

Abstract

At present, most recommender systems involve two stakeholders, providers and customers. Apart from maximizing the recommendation accuracy, the fairness issue for both sides should also be considered. Most of previous studies try to improve two-sided fairness with post-processing algorithms or fairness-aware loss constraints, which are highly dependent on the heuristic adjustments without respect to the optimization goal of accuracy. In contrast, we propose a novel training framework, adaptive weighting towards two-sided fairness-aware recommendation (named Ada2Fair), which lies in the extension of the accuracy-focused objective to a controllable preference learning loss over the interaction data. Specifically, we adjust the optimization scale of an interaction sample with an adaptive weight generator, and estimate the two-sided fairness-aware weights within model training. During the training process, the recommender is trained with two-sided fairness-aware weights to boost the utility of niche providers and inactive customers in a unified way. Extensive experiments on three public datasets verify the effectiveness of Ada2Fair, which can achieve Pareto efficiency in two-sided fairness-aware recommendation.

References

[1]
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. 2017. On sampling strategies for neural network-based collaborative filtering. In SIGKDD. 767–776.
[2]
Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In Proceedings of the 26th international conference on World Wide Web. 183–192.
[3]
Virginie Do, Sam Corbett-Davies, Jamal Atif, and Nicolas Usunier. 2021. Two-sided fairness in rankings via Lorenz dominance. Advances in Neural Information Processing Systems 34 (2021), 8596–8608.
[4]
Fangyu Han, Shumei Wang, Jiayu Zhao, Renhui Wu, Xiaobin Rui, and Zhixiao Wang. 2023. Fair Re-Ranking Recommendation Based on Debiased Multi-graph Representations. In ADMA (1)(Lecture Notes in Computer Science, Vol. 14176). Springer, 168–182.
[5]
WU Haolun, MA Chen, Bhaskar MITRA, DIAZ Fernando, and LIU Xue. 2022. A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation. ACM Transactions on Information Systems (2022).
[6]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648.
[7]
Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20, 4 (2002), 422–446.
[8]
Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In SIGKDD. 1748–1757.
[9]
Jie Li, Yongli Ren, and Ke Deng. 2022. FairGAN: GANs-based fairness-aware learning for recommendations with implicit feedback. In Proceedings of the ACM Web Conference 2022. 297–307.
[10]
Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, and Peng Jiang. 2019. A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In Proceedings of the 13th ACM Conference on recommender systems. 20–28.
[11]
Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, and Haihong Tang. 2020. Diversified interactive recommendation with implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4932–4939.
[12]
Julian McAuley, Jure Leskovec, and Dan Jurafsky. 2012. Learning attitudes and attributes from multi-aspect reviews. In 2012 IEEE 12th International Conference on Data Mining. IEEE, 1020–1025.
[13]
Mohammadmehdi Naghiaei, Hossein A Rahmani, and Yashar Deldjoo. 2022. Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 770–779.
[14]
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on EMNLP-IJCNLP. 188–197.
[15]
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 TheWebConf 2020. 1194–1204.
[16]
Evaggelia Pitoura, Kostas Stefanidis, and Georgia Koutrika. 2022. Fairness in rankings and recommendations: an overview. The VLDB Journal (2022), 1–28.
[17]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452–461.
[18]
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In ICML. PMLR, 1670–1679.
[19]
Jiakai Tang, Shiqi Shen, Zhipeng Wang, Zhi Gong, Jingsen Zhang, and Xu Chen. 2023. When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation. In RecSys. ACM, 200–210.
[20]
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. A survey on the fairness of recommender systems. ACM Transactions on Information Systems 41, 3 (2023), 1–43.
[21]
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.
[22]
Yao Wu, Jian Cao, Guandong Xu, and Yudong Tan. 2021. Tfrom: A two-sided fairness-aware recommendation model for both customers and providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1013–1022.
[23]
Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, and Zhenhua Dong. 2023. P-MMF: Provider Max-min Fairness Re-ranking in Recommender System. In Proceedings of the ACM Web Conference 2023. 3701–3711.
[24]
Lanling Xu, Zhen Tian, Gaowei Zhang, Junjie Zhang, Lei Wang, Bowen Zheng, Yifan Li, Jiakai Tang, Zeyu Zhang, Yupeng Hou, 2023. Towards a more user-friendly and easy-to-use benchmark library for recommender systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2837–2847.
[25]
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, 2022. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4722–4726.
[26]
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2021. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. In CIKM. ACM, 4653–4664.
[27]
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web. 22–32.

Index Terms

  1. Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
    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 the author(s) 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].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 October 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Fairness-aware Recommendation
    2. Recommender Systems

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Funding Sources

    Conference

    Acceptance Rates

    Overall Acceptance Rate 254 of 1,295 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 169
      Total Downloads
    • Downloads (Last 12 months)169
    • Downloads (Last 6 weeks)16
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media