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Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives.
Teleology and Deontology in Ethics
Warren Ashby. 1950 · 1950
Earlier work this paper cites.
Towards an understanding of inequity
J Stacy Adams. 1963 · 1963
Earlier work this paper cites.
Perspectives on Information Overload
Jacob Jacoby. 1984 · 1984
Earlier work this paper cites.
A quantitative measure of fairness and discrimination
Rajendra K Jain, Dah-Ming W Chiu, William R Hawe, et al · 1984
Earlier work this paper cites.
Bias in computer systems
Batya Friedman and Helen Nissenbaum. 1996 · 1996
Earlier work this paper cites.
Anti-Discrimination Rights Without Equality
Elisa Holmes. 2005 · 2005
Earlier work this paper cites.
The Nicomachean ethics
W. D. Ross Aristotle and Lesley Brown. 2009 · 2009
Earlier work this paper cites.
Causal inference in statistics: An overview
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
Fairness through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (Cambridge, Massachusetts) (ITCS ’12) . Association for Computing Machinery, New York, NY, USA, 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW ’14) . Association for Computing Machinery, New York, NY, USA, 677–686
Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014 · 2014
Earlier work this paper cites.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst. 2016 · 2016
Earlier work this paper cites.
The influence of users’ personality traits on satisfaction and attractiveness of diversified recommendation lists. In EMPIRE 2016 Emotions and Personality in Personalized Systems: Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems co-located with ACM Conference on Recommender Systems (RecSys 2016) , Vol. 1680. CEUR-WS, 43–47
Bruce Ferwerda, Mark Graus, Andreu Vall, Marko Tkalcic, and Markus Schedl. 2016 · 2016
Earlier work this paper cites.
Multisided fairness for recommendation
Robin Burke. 2017 · 2017
Earlier work this paper cites.
Ranking with fairness constraints
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017 · 2017
Earlier work this paper cites.
Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017 , Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Why Should Like Cases Be Decided Alike? A Formal Model of Aristotelian Justice
Benjamin Johnson and Richard Jordan. 2017 · 2017
Earlier work this paper cites.
Counterfactual Fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 4069–4079
Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Earlier work this paper cites.
Fairness aware recommendations on behance. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 144–155
Natwar Modani, Deepali Jain, Ujjawal Soni, Gaurav Kumar Gupta, and Palak Agarwal. 2017 · 2017
Earlier work this paper cites.
Fairness in Package-to-Group Recommendations. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 371–379
Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, and Panayiotis Tsaparas. 2017 · 2017
Earlier work this paper cites.
Fairness-Aware Group Recommendation with Pareto-Efficiency. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17) . Association for Computing Machinery, New York, NY, USA, 107–115
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. 2017 · 2017
Earlier work this paper cites.
Measuring Fairness in Ranked Outputs. In Proceedings of the 29th International Conference on Scientific and Statistical Database Management (Chicago, IL, USA) (SSDBM ’17) . Association for Computing Machinery, New York, NY, USA, Article 22, 6 pages
Ke Yang and Julia Stoyanovich. 2017 · 2017
Earlier work this paper cites.
Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang. 2017 · 2017
Earlier work this paper cites.
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1171–1180
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. 2017 · 2017
Earlier work this paper cites.
Fa* ir: A fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1569–1578
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017 · 2017
Earlier work this paper cites.
Equity of Attention: Amortizing Individual Fairness in Rankings. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR ’18) . Association for Computing Machinery, New York, NY, USA, 405–414
Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018 · 2018
Earlier work this paper cites.
Synthetic attribute data for evaluating consumer-side fairness
Robin Burke, Jackson Kontny, and Nasim Sonboli. 2018a · 2018
Earlier work this paper cites.
A fairness-aware hybrid recommender system
Golnoosh Farnadi, Pigi Kouki, Spencer K Thompson, Sriram Srinivasan, and Lise Getoor. 2018 · 2018
Earlier work this paper cites.
Fair Allocation of Indivisible Goods: Improvements and Generalizations. In Proceedings of the 2018 ACM Conference on Economics and Computation (Ithaca, NY, USA) (EC ’18) . Association for Computing Machinery, New York, NY, USA, 539–556
Mohammad Ghodsi, Mohammadtaghi Hajiaghayi, Masoud Seddighin, Saeed Seddighin, and Hadi Yami. 2018 · 2018
Earlier work this paper cites.
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 903–912
Nina Grgic-Hlaca, Elissa M. Redmiles, Krishna P. Gummadi, and Adrian Weller. 2018 · 2018
Earlier work this paper cites.
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (New Orleans, Louisiana, USA) (AAAI’18/IAAI’18/EAAI’18) . AAAI Press, Article 7, 10 pages
Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, and Adrian Weller. 2018 · 2018
Earlier work this paper cites.
Recommendation independence. In Conference on Fairness, Accountability and Transparency . PMLR, 187–201
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2018 · 2018
Earlier work this paper cites.
Using image fairness representations in diversity-based re-ranking for recommendations. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization . 23–28
Chen Karako and Putra Manggala. 2018 · 2018
Earlier work this paper cites.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning . PMLR, 2564–2572
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018 · 2018
Earlier work this paper cites.
User fairness in recommender systems. In Companion Proceedings of the The Web Conference 2018 . 101–102
Jurek Leonhardt, Avishek Anand, and Megha Khosla. 2018 · 2018
Earlier work this paper cites.
Towards a Fair Marketplace: Counterfactual Evaluation of the Trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 2243–2251
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018 · 2018
Earlier work this paper cites.
Translation tutorial: 21 fairness definitions and their politics. In Proc. Conf. Fairness Accountability Transp., New York, USA , Vol. 1170
Arvind Narayanan. 2018 · 2018
Earlier work this paper cites.
Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
Hyejin Shin, Sungwook Kim, Junbum Shin, and Xiaokui Xiao. 2018 · 2018
Earlier work this paper cites.
Fairness of Exposure in Rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 2219–2228
Ashudeep Singh and Thorsten Joachims. 2018 · 2018
Earlier work this paper cites.
Calibrated Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18) . Association for Computing Machinery, New York, NY, USA, 154–162
Harald Steck. 2018 · 2018
Cited alongside, same era.
Algorithmic Glass Ceiling in Social Networks: The Effects of Social Recommendations on Network Diversity. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 923–932
Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau. 2018 · 2018
Cited alongside, same era.
Multistakeholder Recommendation with Provider Constraints. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18) . Association for Computing Machinery, New York, NY, USA, 54–62
Özge Sürer, Robin Burke, and Edward C. Malthouse. 2018 · 2018
Cited alongside, same era.
Fairness-Aware Tensor-Based Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 1153–1162
Fairness and Diversity in Social-Based Recommender Systems. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (Genoa, Italy) (UMAP ’20 Adjunct) . Association for Computing Machinery, New York, NY, USA, 83–88
Dimitris Sacharidis, Carine Pierrette Mukamakuza, and Hannes Werthner. 2020 · 2020
Later among the works it cites.
Opportunistic Multi-aspect Fairness through Personalized Re-ranking. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization . 239–247
Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, and Bamshad Mobasher. 2020 · 2020
Later among the works it cites.
Fair Sequential Group Recommendations. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (Brno, Czech Republic) (SAC ’20) . Association for Computing Machinery, New York, NY, USA, 1443–1452
Maria Stratigi, Jyrki Nummenmaa, Evaggelia Pitoura, and Kostas Stefanidis. 2020 · 2020
Later among the works it cites.
Stable Deep Reinforcement Learning Method by Predicting Uncertainty in Rewards as a Subtask. In Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II (Bangkok, Thailand). Springer-Verlag, Berlin, Heidelberg, 651–662
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Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
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Multi-stakeholder recommendation and its connection to multi-sided fairness
Himan Abdollahpouri and Robin Burke. 2019 · 2019
Cited alongside, same era.
Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2212–2220
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
Cited alongside, same era.
Enhancing Long Term Fairness in Recommendations with Variational Autoencoders. In Proceedings of the 11th International Conference on Management of Digital EcoSystems (Limassol, Cyprus) (MEDES ’19) . Association for Computing Machinery, New York, NY, USA, 95–102
Rodrigo Borges and Kostas Stefanidis. 2019 · 2019
Cited alongside, same era.
Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning . PMLR, 715–724
Avishek Bose and William Hamilton. 2019 · 2019
Cited alongside, same era.
Fairness and transparency in ranking. In ACM SIGIR Forum , Vol. 52. ACM New York, NY, USA, 64–71
Carlos Castillo. 2019 · 2019
Cited alongside, same era.
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 339–348
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell. 2019 · 2019
Cited alongside, same era.
Fair Transfer Learning with Missing Protected Attributes. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19) . Association for Computing Machinery, New York, NY, USA, 91–98
Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, and Supriyo Chakraborty. 2019 · 2019
Cited alongside, same era.
Recommender systems fairness evaluation via generalized cross entropy
Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín, and Tommaso Di Noia. 2019 · 2019
Cited alongside, same era.
Kanata Suzuki and Tetsuya Ogata. 2020 · 2020
Later among the works it cites.
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Later among the works it cites.
Addressing Marketing Bias in Product Recommendations. In Proceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM ’20) . Association for Computing Machinery, New York, NY, USA, 618–626
Mengting Wan, Jianmo Ni, Rishabh Misra, and Julian McAuley. 2020 · 2020
Later among the works it cites.
Robust optimization for fairness with noisy protected groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael I Jordan. 2020 · 2020
Later among the works it cites.
Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes
Shen Yan, Hsien-te Kao, and Emilio Ferrara. 2020 · 2020
Later among the works it cites.
Fairness with overlapping groups; a probabilistic perspective
Forest Yang, Mouhamadou Cisse, and Oluwasanmi O Koyejo. 2020 · 2020
Later among the works it cites.
Reducing Disparate Exposure in Ranking: A Learning To Rank Approach. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 2849–2855
Meike Zehlike and Carlos Castillo. 2020 · 2020
Later among the works it cites.
Large-Scale Causal Approaches to Debiasing Post-Click Conversion Rate Estimation with Multi-Task Learning
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, and Ramin Ramezani. 2020 · 2020
Later among the works it cites.
Explainable recommendation: A survey and new perspectives
Yongfeng Zhang and Xu Chen. 2020 · 2020
Later among the works it cites.
FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs
Qiliang Zhu, Qibo Sun, Zengxiang Li, and Shangguang Wang. 2020a · 2020
Later among the works it cites.
Online certification of preference-based fairness for personalized recommender systems
Virginie Do, Sam Corbett-Davies, Jamal Atif, and Nicolas Usunier. 2021a · 2021
Later among the works it cites.
Two-sided fairness in rankings via Lorenz dominance. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual , Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 8596–8608
Virginie Do, Sam Corbett-Davies, Jamal Atif, and Nicolas Usunier. 2021b · 2021
Later among the works it cites.
Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 436–446
David García-Soriano and Francesco Bonchi. 2021 · 2021
Later among the works it cites.
Towards Long-Term Fairness in Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21) . Association for Computing Machinery, New York, NY, USA, 445–453
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
The Winner Takes It All: Geographic Imbalance and Provider (Un)Fairness in Educational Recommender Systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 1808–1812
Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, and Mirko Marras. 2021 · 2021
Later among the works it cites.
Online Post-Processing in Rankings for Fair Utility Maximization. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21) . Association for Computing Machinery, New York, NY, USA, 454–462
Ananya Gupta, Eric Johnson, Justin Payan, Aditya Kumar Roy, Ari Kobren, Swetasudha Panda, Jean-Baptiste Tristan, and Michael Wick. 2021 · 2021
Later among the works it cites.
Debiasing Career Recommendations with Neural Fair Collaborative Filtering. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 3779–3790
Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, and James Foulds. 2021 · 2021
Later among the works it cites.
Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 1065–1075
Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, and Emine Yilmaz. 2021 · 2021
Later among the works it cites.
User-Oriented Fairness in Recommendation. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 624–632
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021a · 2021
Later among the works it cites.
Contextualized Fairness for Recommender Systems in Premium Scenarios
Yangkun Li, Mohamed-Laid Hedia, Weizhi Ma, Hongyu Lu, Min Zhang, Yiqun Liu, and Shaoping Ma. 2022a · 2021
Later among the works it cites.
A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2021 · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
Later among the works it cites.
SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
Qijie Shen, Wanjie Tao, Jing Zhang, Hong Wen, Zulong Chen, and Quan Lu. 2021 · 2021
Later among the works it cites.
User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms
Ningxia Wang and Li Chen. 2021 · 2021
Later among the works it cites.
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (Virtual Event, Israel) (WSDM ’21) . Association for Computing Machinery, New York, NY, USA, 436–444
Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, and Alex Beutel. 2021 · 2021
Later among the works it cites.
Learning Fair Representations for Recommendation: A Graph-Based Perspective. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 2198–2208
Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang. 2021b · 2021
Later among the works it cites.
TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers
Yao Wu, Jian Cao, Guandong Xu, and Yudong Tan. 2021a · 2021
Later among the works it cites.
Maximizing Marginal Fairness for Dynamic Learning to Rank. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21) . Association for Computing Machinery, New York, NY, USA, 137–145
Tao Yang and Qingyao Ai. 2021 · 2021
Later among the works it cites.
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021 · 2021
Later among the works it cites.
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 3985–3995
Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, and Hongxia Yang. 2021 · 2021
Later among the works it cites.
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22) . Association for Computing Machinery, New York, NY, USA, 316–324
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022 · 2022
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FairGAN: GANs-Based Fairness-Aware Learning for Recommendations with Implicit Feedback. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22) . Association for Computing Machinery, New York, NY, USA, 297–307
Jie Li, Yongli Ren, and Ke Deng. 2022b · 2022
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Understanding and Mitigating the Effect of Outliers in Fair Ranking. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22) . Association for Computing Machinery, New York, NY, USA, 861–869
Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, and Maarten de Rijke. 2022 · 2022
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Enumerating Fair Packages for Group Recommendations. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22) . Association for Computing Machinery, New York, NY, USA, 870–878
Ryoma Sato. 2022 · 2022
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