ABSTRACT
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments.
A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.
- 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 , Vol. 30, 1 (2020), 127--158.Google ScholarCross Ref
- Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 a. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference .Google Scholar
- Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 b. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).Google Scholar
- Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, and Edward Malthouse. 2021. User-centered Evaluation of Popularity Bias in Recommender Systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization . 119--129.Google ScholarDigital Library
- Reuben Binns. 2018. Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency. PMLR, 149--159.Google Scholar
- 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.Google ScholarDigital Library
- Robin Burke. 2017. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017).Google Scholar
- Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 (2020).Google Scholar
- Abhijnan Chakraborty, Aniko Hannak, Asia J Biega, and Krishna P Gummadi. 2017. Fair sharing for sharing economy platforms. (2017).Google Scholar
- 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 preprint arXiv:2010.03240 (2020).Google Scholar
- Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems. 101--109.Google ScholarDigital Library
- Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, and Tommaso Di Noia. 2021 a. A flexible framework for evaluating user and item fairness in recommender systems. User Modeling and User-Adapted Interaction (2021), 1--55.Google Scholar
- Yashar Deldjoo, Alejandro Bellogin, and Tommaso Di Noia. 2021 b. Explaining recommender systems fairness and accuracy through the lens of data characteristics. Information Processing & Management , Vol. 58, 5 (2021), 102662.Google ScholarDigital Library
- 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 , Vol. 34 (2021).Google Scholar
- Qiang Dong, Shuang-Shuang Xie, Xiaofan Yang, and Yuan Yan Tang. 2020. User-item matching for recommendation fairness: a view from item-providers. arXiv preprint arXiv:2009.14474 (2020).Google Scholar
- 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.Google ScholarDigital Library
- Michael D Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2021. Fairness and Discrimination in Information Access Systems. arXiv preprint arXiv:2105.05779 (2021).Google Scholar
- Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et almbox. 2021. Towards Long-term Fairness in Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 445--453.Google ScholarDigital Library
- Elizabeth Gómez, Ludovico Boratto, and Maria Salamó. 2022. Provider fairness across continents in collaborative recommender systems. Information Processing & Management , Vol. 59, 1 (2022), 102719.Google ScholarDigital Library
- Prem Gopalan, Jake M Hofman, and David M Blei. 2015. Scalable Recommendation with Hierarchical Poisson Factorization.. In UAI . 326--335.Google Scholar
- 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.Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining. Ieee, 263--272.Google ScholarDigital Library
- Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, and James Foulds. 2021. Debiasing career recommendations with neural fair collaborative filtering. In Proceedings of the Web Conference 2021. 3779--3790.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Ömer Kirnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, and Emine Yilmaz. 2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of the Web Conference 2021. 1065--1075.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Chen Lin, Xinyi Liu, Guipeng Xv, and Hui Li. 2021. Mitigating Sentiment Bias for Recommender Systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 31--40.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Mohammadmehdi Naghiaei, Hossein A. Rahmani, and Mahdi Dehghan. 2022. The Unfairness of Popularity Bias in Book Recommendation. arXiv preprint arXiv:2202.13446 (2022).Google Scholar
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In 2008 Eighth IEEE International Conference on Data Mining. IEEE, 502--511.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Dino Pedreschi, Salvatore Ruggieri, and Franco Turini. 2009. Measuring discrimination in socially-sensitive decision records. In Proceedings of the 2009 SIAM international conference on data mining. SIAM, 581--592.Google ScholarCross Ref
- Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, and Mohammadmehdi Naghiaei. 2022. The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation. arXiv preprint arXiv:2202.13307 (2022).Google Scholar
- Aghiles Salah, Quoc-Tuan Truong, and Hady W Lauw. 2020. Cornac: A Comparative Framework for Multimodal Recommender Systems. J. Mach. Learn. Res. , Vol. 21 (2020), 95--1.Google Scholar
- Quoc-Tuan Truong, Aghiles Salah, Thanh-Binh Tran, Jingyao Guo, and Hady W Lauw. 2021. Exploring Cross-Modality Utilization in Recommender Systems. IEEE Internet Computing (2021).Google ScholarCross Ref
- Lequn Wang and Thorsten Joachims. 2021. User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 23--41.Google ScholarDigital Library
- Yao Wu, Jian Cao, Guandong Xu, and Yudong Tan. 2021. TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers. arXiv preprint arXiv:2104.09024 (2021).Google Scholar
- 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.Google ScholarCross Ref
- Emre Yalcin and Alper Bilge. 2021. Investigating and counteracting popularity bias in group recommendations. Information Processing & Management , Vol. 58, 5 (2021), 102608.Google ScholarDigital Library
Index Terms
- CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
Recommendations
Multi-view visual Bayesian personalized ranking for restaurant recommendation
AbstractIn recent recommendation systems, the image information of items is often used in conjunction with deep convolution network to directly learn the visual features of items. However, the existing approaches usually use only one image to represent an ...
A Scrutable Algorithm for Enhancing the Efficiency of Recommender Systems using Fuzzy Decision Tree
AICTC '16: Proceedings of the International Conference on Advances in Information Communication Technology & ComputingRecommender system plays the major role of filtering the needed information from enormous amount of overloaded information. From e-commerce to movie websites, recommender systems are being used for market their product to the customer. Also, recommender ...
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. ...
Comments