{"id":"https://openalex.org/W3126453897","doi":"https://doi.org/10.1145/3531146.3533136","title":"Achieving Fairness via Post-Processing in Web-Scale Recommender Systems\u2731","display_name":"Achieving Fairness via Post-Processing in Web-Scale Recommender Systems\u2731","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W3126453897","doi":"https://doi.org/10.1145/3531146.3533136","mag":"3126453897"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533136","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533136","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062116678","display_name":"Preetam Nandy","orcid":"https://orcid.org/0000-0003-3892-9811"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Preetam Nandy","raw_affiliation_strings":["LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068232760","display_name":"Cyrus DiCiccio","orcid":"https://orcid.org/0009-0006-2803-858X"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cyrus DiCiccio","raw_affiliation_strings":["While at LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"While at LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010142303","display_name":"Divya Venugopalan","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Divya Venugopalan","raw_affiliation_strings":["LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062371613","display_name":"Heloise Logan","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heloise Logan","raw_affiliation_strings":["LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078166693","display_name":"Kinjal Basu","orcid":"https://orcid.org/0000-0002-4091-0119"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kinjal Basu","raw_affiliation_strings":["LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073887573","display_name":"Noureddine El Karoui","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noureddine El Karoui","raw_affiliation_strings":["While at LinkedIn Corporation, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"While at LinkedIn Corporation, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5062116678"],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":4.1249,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.95712017,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"715","last_page":"725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9797000288963318,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.9515372514724731},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8112512826919556},{"id":"https://openalex.org/keywords/odds","display_name":"Odds","score":0.7556689977645874},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.723496675491333},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5567780137062073},{"id":"https://openalex.org/keywords/web-application","display_name":"Web application","score":0.4276653826236725},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.35889291763305664},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24082884192466736},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2055085301399231},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.09635403752326965}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.9515372514724731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8112512826919556},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.7556689977645874},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.723496675491333},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5567780137062073},{"id":"https://openalex.org/C118643609","wikidata":"https://www.wikidata.org/wiki/Q189210","display_name":"Web application","level":2,"score":0.4276653826236725},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.35889291763305664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24082884192466736},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2055085301399231},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.09635403752326965},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3531146.3533136","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533136","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1961345416","https://openalex.org/W1979769549","https://openalex.org/W1999010044","https://openalex.org/W2014352947","https://openalex.org/W2071906052","https://openalex.org/W2097829006","https://openalex.org/W2100960835","https://openalex.org/W2120865735","https://openalex.org/W2143117649","https://openalex.org/W2162670686","https://openalex.org/W2507134384","https://openalex.org/W2530395818","https://openalex.org/W2540757487","https://openalex.org/W2584805976","https://openalex.org/W2769473018","https://openalex.org/W2787991113","https://openalex.org/W2788284633","https://openalex.org/W2795524876","https://openalex.org/W2809111384","https://openalex.org/W2883147591","https://openalex.org/W2911765495","https://openalex.org/W2946294136","https://openalex.org/W2950173087","https://openalex.org/W2963106283","https://openalex.org/W2963174898","https://openalex.org/W2963178340","https://openalex.org/W2963327716","https://openalex.org/W2963779314","https://openalex.org/W2963919086","https://openalex.org/W2964427276","https://openalex.org/W2971071571","https://openalex.org/W3012600133","https://openalex.org/W3013778941","https://openalex.org/W3014590323","https://openalex.org/W3023309920","https://openalex.org/W3028135017","https://openalex.org/W3101148092","https://openalex.org/W3101935024","https://openalex.org/W3102518922","https://openalex.org/W3103891807","https://openalex.org/W3184111600","https://openalex.org/W3192198661","https://openalex.org/W3193160514","https://openalex.org/W4238665603","https://openalex.org/W4288079518","https://openalex.org/W4289258088","https://openalex.org/W6959643953"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2968745142","https://openalex.org/W2809363009","https://openalex.org/W2348159088","https://openalex.org/W2045871438","https://openalex.org/W2499363748","https://openalex.org/W2350747448","https://openalex.org/W2368095327","https://openalex.org/W2122731942","https://openalex.org/W2402445420"],"abstract_inverted_index":{"Building":[0],"fair":[1],"recommender":[2,31,90,120],"systems":[3],"is":[4],"a":[5,110],"challenging":[6],"and":[7,37,73],"crucial":[8],"area":[9],"of":[10,23,28,35,54,71,81,135],"study":[11],"due":[12],"to":[13,30,116,131],"its":[14],"immense":[15],"impact":[16],"on":[17,104],"society.":[18],"We":[19,64,122],"extended":[20],"the":[21,79,98,105,133],"definitions":[22],"two":[24],"commonly":[25,85],"accepted":[26],"notions":[27],"fairness":[29,41],"systems,":[32],"namely":[33],"equality":[34,70],"opportunity":[36,72],"equalized":[38,74],"odds.":[39],"These":[40],"measures":[42],"ensure":[43],"that":[44,100],"equally":[45,52],"\u201cqualified\u201d":[46],"(or":[47],"\u201cunqualified\u201d)":[48],"candidates":[49],"are":[50,94],"treated":[51],"regardless":[53],"their":[55],"protected":[56],"attribute":[57],"status":[58],"(such":[59],"as":[60,126,128],"gender":[61],"or":[62],"race).":[63],"propose":[65],"scalable":[66],"methods":[67],"for":[68],"achieving":[69],"odds":[75],"in":[76,78,97],"rankings":[77],"presence":[80],"position":[82],"bias,":[83],"which":[84],"plagues":[86],"data":[87],"generated":[88],"from":[89],"systems.":[91,121],"Our":[92],"algorithms":[93],"model":[95],"agnostic":[96],"sense":[99],"they":[101],"depend":[102],"only":[103],"final":[106],"scores":[107],"provided":[108],"by":[109],"model,":[111],"making":[112],"them":[113],"easily":[114],"applicable":[115],"virtually":[117],"all":[118],"web-scale":[119],"conduct":[123],"extensive":[124],"simulations":[125],"well":[127],"real-world":[129],"experiments":[130],"show":[132],"efficacy":[134],"our":[136],"approach.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2025-10-10T00:00:00"}