{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:20:19Z","timestamp":1782145219816,"version":"3.54.5"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Ranking algorithms in online platforms serve not only users on the demand side, but also items on the supply side. While ranking has traditionally presented items in an order that maximizes their utility to users, the uneven interactions that different items receive as a result of such a ranking can pose item fairness concerns. Moreover, interaction is affected by various forms of bias, two of which have received considerable attention: position bias and selection bias. Position bias occurs due to lower likelihood of observation for items in lower ranked positions. Selection bias occurs because interaction is not possible with items below an arbitrary cutoff position chosen by the front-end application at deployment time (i.e., showing only the top-\n            <jats:italic>k<\/jats:italic>\n            items). A less studied, third form of bias, trust bias, is equally important, as it makes interaction dependent on rank even after observation, by influencing the item\u2019s perceived relevance. To capture interaction disparity in the presence of all three biases, in this article, we introduce a flexible fairness metric. Using this metric, we develop a post-processing algorithm that optimizes fairness in ranking through greedy exploration and allows a tradeoff between fairness and utility. Our algorithm outperforms state-of-the-art fair ranking algorithms on several datasets.\n          <\/jats:p>","DOI":"10.1145\/3652864","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T10:20:02Z","timestamp":1712398802000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Fairness of Interaction in Ranking under Position, Selection, and Trust Bias"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7165-4841","authenticated-orcid":false,"given":"Zohreh","family":"Ovaisi","sequence":"first","affiliation":[{"name":"University of Illinois Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2629-4495","authenticated-orcid":false,"given":"Parsa","family":"Saadatpanah","sequence":"additional","affiliation":[{"name":"Meta Inc, Washington DC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4977-2298","authenticated-orcid":false,"given":"Shahin","family":"Sefati","sequence":"additional","affiliation":[{"name":"Meta Inc, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6479-9769","authenticated-orcid":false,"given":"Mesrob","family":"Ohannessian","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7662-2568","authenticated-orcid":false,"given":"Elena","family":"Zheleva","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Epinion dataset. Retrieved 3 February 2023 from https:\/\/www.shopping.com\/. (n.d.)."},{"key":"e_1_3_2_3_2","unstructured":"MovieLens dataset. Retrieved 3 February 2023 from https:\/\/movielens.org\/. (n.d.)."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313697"},{"key":"e_1_3_2_5_2","article-title":"Unbiased learning to rank with unbiased propensity estimation","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W Bruce Croft. 2018. Unbiased learning to rank with unbiased propensity estimation. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (2018).","journal-title":"In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval"},{"key":"e_1_3_2_6_2","unstructured":"Kinjal Basu Cyrus DiCiccio Heloise Logan and Noureddine El Karoui. 2020. A framework for fairness in two-sided marketplaces. arXiv:2006.12756. Retrieved from https:\/\/arxiv.org\/abs\/2006.12756"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/13"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.83"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273513"},{"key":"e_1_3_2_10_2","article-title":"Ranking with fairness constraints","author":"Celis L. Elisa","year":"2018","unstructured":"L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi. 2018. Ranking with fairness constraints. International Colloquium on Automata, Languages and Programming (2018).","journal-title":"International Colloquium on Automata, Languages and Programming"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240370"},{"key":"e_1_3_2_12_2","unstructured":"Dua Dheeru and Efi Karra Taniskidou. 2017. UCI machine learning repository. 12 (2017). Retrieved 3 February 2023 from http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v16i1.19284"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2004.1365067"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Y. Ge S. Liu R. Gao Y. Xian Y. Li X. Zhao C. Pei F. Sun J. Ge W. Ou and Y. Zhang. 2021. Towards long-term fairness in recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 445\u2013453.","DOI":"10.1145\/3437963.3441824"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Y. Ge X. Zhao L. Yu S. Paul D. Hu C.-C. Hsieh and Y. Zhang. 2022. 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. 316\u2013324.","DOI":"10.1145\/3488560.3498487"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330691"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441724"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157469"},{"key":"e_1_3_2_21_2","article-title":"The movielens datasets: History and context","author":"Harper F. Maxwell","year":"2015","unstructured":"F. Maxwell Harper and Joseph A. Konstan. 2015. The movielens datasets: History and context. ACM Trans (2015).","journal-title":"ACM Trans"},{"key":"e_1_3_2_22_2","volume-title":"ProPublica","author":"Mattu Lauren Kirchner Jeff Larson, Surya","year":"2016","unstructured":"Lauren Kirchner Jeff Larson, Surya Mattu and Julia Angwin. 2016. How we analyzed the compas recidivism algorithm. In ProPublica."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018699"},{"key":"e_1_3_2_24_2","first-page":"1650","volume-title":"Proceedings of the AAAI.","author":"Krause Andreas","year":"2007","unstructured":"Andreas Krause and Carlos Guestrin. 2007. Near-optimal observation selection using submodular functions. In Proceedings of the AAAI.. 1650\u20131654."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449866"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462966"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_28_2","article-title":"A survey on bias and fairness in machine learning","author":"Mehrabi Ninareh","year":"2019","unstructured":"Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. ACM Computing Surveys (2019).","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57529-2_12"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401100"},{"key":"e_1_3_2_31_2","article-title":"CPFair: Personalized consumer and producer fairness re-ranking for recommender systems","author":"Naghiaei Mohammadmehdi","year":"2022","unstructured":"Mohammadmehdi Naghiaei, Hossein Rahmani, and Yashar Deldjoo. 2022. CPFair: Personalized consumer and producer fairness re-ranking for recommender systems. In Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval.","journal-title":"In Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01588971"},{"key":"e_1_3_2_33_2","article-title":"Policy-aware unbiased learning to rank for top-k rankings","author":"Oosterhuis Harrie","year":"2020","unstructured":"Harrie Oosterhuis and Maarten de Rijke. 2020. Policy-aware unbiased learning to rank for top-k rankings. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.","journal-title":"In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441794"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380255"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380196"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220088"},{"key":"e_1_3_2_38_2","volume-title":"Proceedings of the Conference on Neural Information Processing Systems.","author":"Singh Ashudeep","year":"2019","unstructured":"Ashudeep Singh and Thorsten Joachims. 2019. Policy learning for fairness in ranking. In Proceedings of the Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_39_2","article-title":"Fairness in ranking under uncertainty","author":"Singh Ashudeep","year":"2021","unstructured":"Ashudeep Singh, David Kempe, and Thorsten Joachims. 2021. Fairness in ranking under uncertainty. Advances in Neural Information Processing Systems (2021).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240372"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330793"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482275"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412031"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3471158.3472260"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159732"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462882"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449901"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132938"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3380048"},{"key":"e_1_3_2_50_2","unstructured":"Ziwei Zhu Jianling Wang and James Caverlee. 2021. Fairness-aware personalized ranking recommendation via adversarial learning. arXiv:2103.07849. Retrieved from https:\/\/arxiv.org\/abs\/2103.07849"}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652864","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652864","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:53:56Z","timestamp":1750287236000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6,30]]}},"alternative-id":["10.1145\/3652864"],"URL":"https:\/\/doi.org\/10.1145\/3652864","relation":{},"ISSN":["2770-6699"],"issn-type":[{"value":"2770-6699","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"2023-03-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}