{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:16:36Z","timestamp":1780431396322,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bundesministerium f\u00fcr Bildung und Forschung","award":["21INVI0803"],"award-info":[{"award-number":["21INVI0803"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,26]]},"DOI":"10.1145\/3706598.3713155","type":"proceedings-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T04:29:11Z","timestamp":1745468951000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["The Effect of Gender De-biased Recommendations \u2014 A User Study on Gender-specific Preferences"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3830-7708","authenticated-orcid":false,"given":"Thorsten","family":"Krause","sequence":"first","affiliation":[{"name":"German Research Center for Artificial Intelligence, Osnabr\u00fcck, Germany and Radboud University, Nijmegen, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2780-9788","authenticated-orcid":false,"given":"Lorena","family":"G\u00f6ritz","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence, Osnabr\u00fcck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1477-4379","authenticated-orcid":false,"given":"Robin","family":"Gratz","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence, Osnabr\u00fcck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_3_3_2_2","doi-asserted-by":"crossref","unstructured":"Oscar Alvarado Nyi\u00a0Nyi Htun Yucheng Jin and Katrien Verbert. 2022. A systematic review of interaction design strategies for group recommendation systems. Proceedings of the ACM on Human-Computer Interaction 6 CSCW2 (2022) 1\u201351.","DOI":"10.1145\/3555161"},{"key":"e_1_3_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Ashwathy Ashokan and Christian Haas. 2021. Fairness metrics and bias mitigation strategies for rating predictions. Information Processing & Management 58 5 (2021) 102646.","DOI":"10.1016\/j.ipm.2021.102646"},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"crossref","unstructured":"Hans\u00a0H Bauer Nicola\u00a0E Sauer and Christine Becker. 2006. Investigating the relationship between product involvement and consumer decision-making styles. Journal of Consumer Behaviour 5 4 (2006) 342\u2013354.","DOI":"10.1002\/cb.185"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450614.3461682"},{"key":"e_1_3_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330745"},{"key":"e_1_3_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864724"},{"key":"e_1_3_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959158"},{"key":"e_1_3_3_3_10_2","doi-asserted-by":"publisher","unstructured":"Daniel\u00a0L. Chen Martin Schonger and Chris Wickens. 2016. oTree\u2014An open-source platform for laboratory online and field experiments. 9 (2016) 88\u201397. 10.1016\/j.jbef.2015.12.001","DOI":"10.1016\/j.jbef.2015.12.001"},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"crossref","unstructured":"Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41 3 (2023) 1\u201339.","DOI":"10.1145\/3564284"},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"crossref","unstructured":"Zhilong Chen Jinghua Piao Xiaochong Lan Hancheng Cao Chen Gao Zhicong Lu and Yong Li. 2022. Practitioners versus users: A value-sensitive evaluation of current industrial recommender system design. Proceedings of the ACM on Human-Computer Interaction 6 CSCW2 (2022) 1\u201332.","DOI":"10.1145\/3555646"},{"key":"e_1_3_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442442.3452325"},{"key":"e_1_3_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781003278290-44"},{"key":"e_1_3_3_3_16_2","doi-asserted-by":"publisher","unstructured":"Karlijn Dinnissen and Christine Bauer. 2022. Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review. Frontiers in Big Data 5 (2022). 10.3389\/fdata.2022.913608","DOI":"10.3389\/fdata.2022.913608"},{"key":"e_1_3_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20606"},{"key":"e_1_3_3_3_18_2","doi-asserted-by":"crossref","unstructured":"Alice\u00a0H Eagly and Wendy Wood. 2012. Social role theory. Handbook of theories of social psychology 2 (2012) 458\u2013476.","DOI":"10.4135\/9781446249222.n49"},{"key":"e_1_3_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/642611.642652"},{"key":"e_1_3_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640457.3688163"},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3406522.3446033"},{"key":"e_1_3_3_3_22_2","unstructured":"Peter Glick and Susan\u00a0T Fiske. 1999. Gender power dynamics and social interaction. Revisioning gender 5 (1999) 365\u2013398."},{"key":"e_1_3_3_3_23_2","volume-title":"Advances in Neural Information Processing Systems","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems , D.\u00a0Lee, M.\u00a0Sugiyama, U.\u00a0Luxburg, I.\u00a0Guyon, and R.\u00a0Garnett (Eds.), Vol.\u00a029. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2016\/file\/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf"},{"key":"e_1_3_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3631700.3664897"},{"key":"e_1_3_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449904"},{"key":"e_1_3_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445161"},{"key":"e_1_3_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3314288"},{"key":"e_1_3_3_3_28_2","unstructured":"Nicolas Jones and Pearl Pu. 2008. User acceptance issues in music recommender systems. (2008)."},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"crossref","unstructured":"Faisal Kamiran and Toon Calders. 2012. Data preprocessing techniques for classification without discrimination. Knowledge and information systems 33 1 (2012) 1\u201333.","DOI":"10.1007\/s10115-011-0463-8"},{"key":"e_1_3_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3213586.3226206"},{"key":"e_1_3_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412232"},{"key":"e_1_3_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3580863"},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"publisher","unstructured":"Thorsten Krause Alina Deriyeva Jan\u00a0H. Beinke Gerrit\u00a0Y. Bartels and Oliver Thomas. 2024. Mitigating Exposure Bias in Recommender Systems\u2014A Comparative Analysis of Discrete Choice Models. ACM Trans. Recomm. Syst. 3 2 Article 19 (Nov. 2024) 37\u00a0pages. 10.1145\/3641291","DOI":"10.1145\/3641291"},{"key":"e_1_3_3_3_35_2","doi-asserted-by":"publisher","unstructured":"Yunqi Li Hanxiong Chen Shuyuan Xu Yingqiang Ge Juntao Tan Shuchang Liu and Yongfeng Zhang. 2023. Fairness in Recommendation: Foundations Methods and Applications. ACM Trans. Intell. Syst. Technol. 14 5 Article 95 (Oct. 2023) 48\u00a0pages. 10.1145\/3610302","DOI":"10.1145\/3610302"},{"key":"e_1_3_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462966"},{"key":"e_1_3_3_3_37_2","doi-asserted-by":"publisher","unstructured":"Haifeng Liu Yukai Wang Hongfei Lin Bo Xu and Nan Zhao. 2022. Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems. Neural Computing and Applications 34 20 (2022) 18097\u201318111. 10.1007\/s00521-022-07373-4","DOI":"10.1007\/s00521-022-07373-4"},{"key":"e_1_3_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474242"},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.3929\/ethz-b-000521574"},{"key":"e_1_3_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412152"},{"key":"e_1_3_3_3_41_2","volume-title":"The thirty-third international flairs conference","author":"Mansoury Masoud","year":"2020","unstructured":"Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, and Bamshad Mobasher. 2020. Investigating potential factors associated with gender discrimination in collaborative recommender systems. In The thirty-third international flairs conference."},{"key":"e_1_3_3_3_42_2","doi-asserted-by":"publisher","unstructured":"Alessandro\u00a0B. 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 58 5 (2021) 102666. 10.1016\/j.ipm.2021.102666","DOI":"10.1016\/j.ipm.2021.102666"},{"key":"e_1_3_3_3_43_2","first-page":"746","volume-title":"Proceedings of the 2013 conference of the north american chapter of the association for computational linguistics: Human language technologies","author":"Mikolov Tom\u00e1\u0161","year":"2013","unstructured":"Tom\u00e1\u0161 Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 conference of the north american chapter of the association for computational linguistics: Human language technologies. 746\u2013751."},{"key":"e_1_3_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025727"},{"key":"e_1_3_3_3_45_2","doi-asserted-by":"publisher","unstructured":"Evaggelia Pitoura Kostas Stefanidis and Georgia Koutrika. 2022. Fairness in rankings and recommendations: an overview. The VLDB Journal 31 3 (2022) 431\u2013458. 10.1007\/s00778-021-00697-y","DOI":"10.1007\/s00778-021-00697-y"},{"key":"e_1_3_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291002"},{"key":"e_1_3_3_3_47_2","volume-title":"CHI Conference on Human Factors in Computing Systems","author":"Robinson Katherine-Marie","year":"2024","unstructured":"Katherine-Marie Robinson, Violet Turri, Carol\u00a0J Smith, and Shannon\u00a0K Gallagher. 2024. Tales from the Wild West: Crafting Scenarios to Audit Bias in LLMs. In CHI Conference on Human Factors in Computing Systems."},{"key":"e_1_3_3_3_48_2","doi-asserted-by":"publisher","unstructured":"Clara Rus Jeffrey Luppes Harrie Oosterhuis and Gido\u00a0H. Schoenmacker. 2022. Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. 10.48550\/arXiv.2209.09592 arxiv:https:\/\/arXiv.org\/abs\/2209.09592 [cs]","DOI":"10.48550\/arXiv.2209.09592"},{"key":"e_1_3_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642036"},{"key":"e_1_3_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054609"},{"key":"e_1_3_3_3_51_2","unstructured":"Shrikant Saxena and Shweta Jain. 2024. Exploring and mitigating gender bias in book recommender systems with explicit feedback. Journal of Intelligent Information Systems (2024) 1\u201322."},{"key":"e_1_3_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581017"},{"key":"e_1_3_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939814"},{"key":"e_1_3_3_3_54_2","doi-asserted-by":"publisher","unstructured":"Janet\u00a0K. Swim Kathryn\u00a0J. Aikin Wayne\u00a0S. Hall and Barbara\u00a0A. Hunter. 1995. Sexism and racism: Old-fashioned and modern prejudices. Journal of Personality and Social Psychology 68 2 (1995) 199\u2013214. 10.1037\/0022-3514.68.2.199Place: US Publisher: American Psychological Association.","DOI":"10.1037\/0022-3514.68.2.199"},{"key":"e_1_3_3_3_55_2","doi-asserted-by":"crossref","unstructured":"Robert\u00a0J Tibshirani and Bradley Efron. 1993. An introduction to the bootstrap. Monographs on statistics and applied probability 57 1 (1993) 1\u2013436.","DOI":"10.1007\/978-1-4899-4541-9_1"},{"key":"e_1_3_3_3_56_2","unstructured":"Deanne Tockey and Maria Ignatova. 2019. Gender Insights Report: How women find jobs differently. LinkedIn Talent Solutions https:\/\/business. linkedin. com\/content\/dam\/me\/business\/en-us\/talent-solutions-lodestone\/body\/pdf\/Gender-Insights-Report. pdf (2019)."},{"key":"e_1_3_3_3_57_2","doi-asserted-by":"publisher","unstructured":"Carlo Tomasetto and Sara Appoloni. 2013. A lesson not to be learned? Understanding stereotype threat does not protect women from stereotype threat. Social Psychology of Education 16 2 (2013) 199\u2013213. 10.1007\/s11218-012-9210-6","DOI":"10.1007\/s11218-012-9210-6"},{"key":"e_1_3_3_3_58_2","unstructured":"A Tuckett and F Aldridge. 2009. The NIACE Survey on Adult Participation in Learning 2009: Narrowing Participation."},{"key":"e_1_3_3_3_59_2","volume-title":"Advances in Neural Information Processing Systems","author":"Valizadegan Hamed","year":"2009","unstructured":"Hamed Valizadegan, Rong Jin, Ruofei Zhang, and Jianchang Mao. 2009. Learning to Rank by Optimizing NDCG Measure. In Advances in Neural Information Processing Systems , Y.\u00a0Bengio, D.\u00a0Schuurmans, J.\u00a0Lafferty, C.\u00a0Williams, and A.\u00a0Culotta (Eds.), Vol.\u00a022. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2009\/file\/b3967a0e938dc2a6340e258630febd5a-Paper.pdf"},{"key":"e_1_3_3_3_60_2","doi-asserted-by":"crossref","unstructured":"Yixin Wan George Pu Jiao Sun Aparna Garimella Kai-Wei Chang and Nanyun Peng. 2023. \"Kelly is a Warm Person Joseph is a Role Model\": Gender Biases in LLM-generated Reference Letters. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.09219 (2023).","DOI":"10.18653\/v1\/2023.findings-emnlp.243"},{"key":"e_1_3_3_3_61_2","doi-asserted-by":"publisher","unstructured":"Clarice Wang Kathryn Wang Andrew\u00a0Y. Bian Rashidul Islam Kamrun\u00a0Naher Keya James Foulds and Shimei Pan. 2023. When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation. ACM Trans. Interact. Intell. Syst. 13 3 Article 17 (Sept. 2023) 28\u00a0pages. 10.1145\/3611313","DOI":"10.1145\/3611313"},{"key":"e_1_3_3_3_62_2","unstructured":"Jane Webster and Richard\u00a0T Watson. 2002. Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly (2002) xiii\u2013xxiii."},{"key":"e_1_3_3_3_63_2","doi-asserted-by":"crossref","unstructured":"Candace West and Don\u00a0H Zimmerman. 1987. Doing gender. Gender & society 1 2 (1987) 125\u2013151.","DOI":"10.1177\/0891243287001002002"},{"key":"e_1_3_3_3_64_2","doi-asserted-by":"publisher","unstructured":"Chuhan Wu Fangzhao Wu Xiting Wang Yongfeng Huang and Xing Xie. 2021. Fairness-aware News Recommendation with Decomposed Adversarial Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35 5 (May 2021) 4462\u20134469. 10.1609\/aaai.v35i5.16573","DOI":"10.1609\/aaai.v35i5.16573"},{"key":"e_1_3_3_3_65_2","doi-asserted-by":"crossref","unstructured":"Yao Wu Jian Cao and Guandong Xu. 2023. Fairness in recommender systems: evaluation approaches and assurance strategies. ACM Transactions on Knowledge Discovery from Data 18 1 (2023) 1\u201337.","DOI":"10.1145\/3604558"},{"key":"e_1_3_3_3_66_2","unstructured":"Sirui Yao and Bert Huang. 2017. Beyond Parity: Fairness Objectives for Collaborative Filtering. 30 (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/e6384711491713d29bc63fc5eeb5ba4f-Paper.pdf"},{"key":"e_1_3_3_3_67_2","unstructured":"Muhammad\u00a0Bilal Zafar Isabel Valera Manuel Gomez-Rodriguez and Krishna\u00a0P Gummadi. 2019. Fairness constraints: A flexible approach for fair classification. Journal of Machine Learning Research 20 75 (2019) 1\u201342."},{"key":"e_1_3_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"}],"event":{"name":"CHI 2025: CHI Conference on Human Factors in Computing Systems","location":"Yokohama Japan","acronym":"CHI '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706598.3713155","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706598.3713155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:21:42Z","timestamp":1751606502000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706598.3713155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":67,"alternative-id":["10.1145\/3706598.3713155","10.1145\/3706598"],"URL":"https:\/\/doi.org\/10.1145\/3706598.3713155","relation":{},"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"2025-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}