{"id":"https://openalex.org/W3129302614","doi":"https://doi.org/10.1145/3442381.3449901","title":"Maximizing Marginal Fairness for Dynamic Learning to Rank","display_name":"Maximizing Marginal Fairness for Dynamic Learning to Rank","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3129302614","doi":"https://doi.org/10.1145/3442381.3449901","mag":"3129302614"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449901","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449901","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449901","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tao Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Yang","raw_affiliation_strings":["University of Utah, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":null,"display_name":"Qingyao Ai","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qingyao Ai","raw_affiliation_strings":["University of Utah, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":7.3459,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.97145823,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"137","last_page":"145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9947999715805054,"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"}},{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.9812999963760376,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.8223999738693237},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7803000211715698},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5827999711036682},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5333999991416931},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.45249998569488525},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.42080000042915344},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.40939998626708984}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8223999738693237},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7803000211715698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6534000039100647},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5827999711036682},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5333999991416931},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.42080000042915344},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C66887028","wikidata":"https://www.wikidata.org/wiki/Q382444","display_name":"Marginal cost","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.3549000024795532},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.29339998960494995},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26910001039505005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2538999915122986},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3442381.3449901","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449901","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.09670","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.09670","pdf_url":"https://arxiv.org/pdf/2102.09670","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449901","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449901","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1974360117","https://openalex.org/W1992549066","https://openalex.org/W2026784708","https://openalex.org/W2047221353","https://openalex.org/W2047952076","https://openalex.org/W2048045485","https://openalex.org/W2096438175","https://openalex.org/W2152314154","https://openalex.org/W2155587858","https://openalex.org/W2219888463","https://openalex.org/W2340526403","https://openalex.org/W2507134384","https://openalex.org/W2544318541","https://openalex.org/W2704480242","https://openalex.org/W2739826497","https://openalex.org/W2739916191","https://openalex.org/W2759708284","https://openalex.org/W2769473018","https://openalex.org/W2787991113","https://openalex.org/W2797400361","https://openalex.org/W2905569957","https://openalex.org/W2955421345","https://openalex.org/W3012600133","https://openalex.org/W3012903288","https://openalex.org/W3028722847","https://openalex.org/W3138773240","https://openalex.org/W4206671592"],"related_works":[],"abstract_inverted_index":{"Rankings,":[0],"especially":[1],"those":[2],"in":[3,78,83,93,107,170,198],"search":[4],"and":[5,14,27,118,132,149,168,174,191,201],"recommendation":[6],"systems,":[7],"often":[8,99],"determine":[9],"how":[10,15,22],"people":[11],"access":[12],"information":[13,16,30],"is":[17,32],"exposed":[18],"to":[19,23,80,96,163,194],"people.":[20],"Therefore,":[21],"balance":[24],"the":[25,37,101,113,121,159,165,195],"relevance":[26,54,117,148,167,200],"fairness":[28,65,82,92,103,119,151,169,202],"of":[29,36,104,115,161],"exposure":[31,106],"considered":[33],"as":[34],"one":[35],"key":[38],"problems":[39],"for":[40,146,203],"modern":[41],"IR":[42],"systems.":[43],"As":[44],"conventional":[45],"ranking":[46,64,70,134],"frameworks":[47],"that":[48,157],"myopically":[49],"sorts":[50],"documents":[51,162],"with":[52,179],"their":[53],"will":[55],"inevitably":[56],"introduce":[57],"unfair":[58],"result":[59,73,116],"exposure,":[60],"recent":[61],"studies":[62,90],"on":[63,68,91,120,182],"mostly":[66],"focus":[67],"dynamic":[69,94],"paradigms":[71],"where":[72],"rankings":[74],"can":[75],"be":[76],"adapted":[77],"real-time":[79],"support":[81],"groups":[84],"(i.e.,":[85],"races,":[86],"genders,":[87],"etc.).":[88],"Existing":[89],"learning":[95],"rank,":[97],"however,":[98],"achieve":[100],"overall":[102],"document":[105],"ranked":[108],"lists":[109],"by":[110],"significantly":[111],"sacrificing":[112],"performance":[114],"top":[122],"results.":[123,172],"To":[124],"address":[125],"this":[126],"problem,":[127],"we":[128],"propose":[129],"a":[130],"fair":[131],"unbiased":[133,144],"method":[135,187],"named":[136],"Maximal":[137],"Marginal":[138],"Fairness":[139],"(MMF).":[140],"The":[141],"algorithm":[142],"integrates":[143],"estimators":[145],"both":[147,199],"merit-based":[150],"while":[152],"providing":[153],"an":[154],"explicit":[155],"controller":[156],"balances":[158],"selection":[160],"maximize":[164],"marginal":[166],"top-k":[171,204],"Theoretical":[173],"empirical":[175],"analysis":[176],"shows":[177],"that,":[178],"small":[180],"compromises":[181],"long":[183],"list":[184],"fairness,":[185],"our":[186],"achieves":[188],"superior":[189],"efficiency":[190],"effectiveness":[192],"comparing":[193],"state-of-the-art":[196],"algorithms":[197],"rankings.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2021-03-01T00:00:00"}
