{"id":"https://openalex.org/W4284673705","doi":"https://doi.org/10.1145/3477495.3531892","title":"A Meta-learning Approach to Fair Ranking","display_name":"A Meta-learning Approach to Fair Ranking","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4284673705","doi":"https://doi.org/10.1145/3477495.3531892"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531892","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531892","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","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/A5002438548","display_name":"Yuan Wang","orcid":"https://orcid.org/0000-0002-4951-4286"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Wang","raw_affiliation_strings":["Santa Clara University, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Santa Clara University, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I16269868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021486404","display_name":"Zhiqiang Tao","orcid":"https://orcid.org/0000-0002-5639-7540"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqiang Tao","raw_affiliation_strings":["Santa Clara University, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Santa Clara University, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I16269868"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101972978","display_name":"Yi Fang","orcid":"https://orcid.org/0000-0001-6572-4315"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Fang","raw_affiliation_strings":["Santa Clara University, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Santa Clara University, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I16269868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16269868"],"apc_list":null,"apc_paid":null,"fwci":1.1808,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80483469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2539","last_page":"2544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9864000082015991,"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"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9864000082015991,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.8569499254226685},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.830924391746521},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.7878320217132568},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.739302396774292},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6457046270370483},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6425371766090393},{"id":"https://openalex.org/keywords/ranking-svm","display_name":"Ranking SVM","score":0.5766593217849731},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5751832723617554},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.540303111076355},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5160707831382751},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4461961090564728},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40554162859916687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13443639874458313},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.09003052115440369}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8569499254226685},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.830924391746521},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.7878320217132568},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739302396774292},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6457046270370483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6425371766090393},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.5766593217849731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5751832723617554},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.540303111076355},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5160707831382751},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4461961090564728},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40554162859916687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13443639874458313},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.09003052115440369},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3531892","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531892","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2006447892","https://openalex.org/W2108862644","https://openalex.org/W2115584760","https://openalex.org/W2336485875","https://openalex.org/W2544318541","https://openalex.org/W2704480242","https://openalex.org/W2962762307","https://openalex.org/W2963189767","https://openalex.org/W2964751853","https://openalex.org/W2969843106","https://openalex.org/W2985745615","https://openalex.org/W3012903288","https://openalex.org/W3092499828","https://openalex.org/W3102092462","https://openalex.org/W3153906321","https://openalex.org/W3164238513","https://openalex.org/W3165956705","https://openalex.org/W4212774754","https://openalex.org/W4230692091","https://openalex.org/W4300482433"],"related_works":["https://openalex.org/W3127142483","https://openalex.org/W2036613096","https://openalex.org/W2138488530","https://openalex.org/W2963493716","https://openalex.org/W4385565564","https://openalex.org/W2104465941","https://openalex.org/W2293317945","https://openalex.org/W4318960487","https://openalex.org/W1786507113","https://openalex.org/W161037631"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"the":[3,16,26,40,45,50,53,62,74,77,96,132,150,156,161,164,168,173,185,197,208],"fairness":[4,209],"in":[5,39],"information":[6],"retrieval":[7],"(IR)":[8],"system":[9],"has":[10],"received":[11],"increasing":[12],"research":[13],"attention.":[14],"While":[15],"data-driven":[17],"ranking":[18,41,151,169,203],"models":[19,32],"achieve":[20],"significant":[21],"improvements":[22],"over":[23],"traditional":[24],"methods,":[25],"dataset":[27,48,121,135],"used":[28],"to":[29,58,112,148],"train":[30,114,125],"such":[31,66],"is":[33],"usually":[34,56],"biased,":[35],"which":[36,71],"causes":[37],"unfairness":[38],"models.":[42],"For":[43],"example,":[44],"collected":[46],"imbalance":[47],"on":[49,61,131,155,191],"subject":[51],"of":[52,163],"expert":[54],"search":[55],"leads":[57],"systematic":[59],"discrimination":[60],"specific":[63],"demographic":[64],"groups":[65,101],"as":[67,144,176],"race,":[68],"gender,":[69],"etc,":[70],"further":[72],"reduces":[73],"exposure":[75],"for":[76,99],"minority":[78,157],"group.":[79,158],"To":[80,159],"solve":[81,182],"this":[82],"problem,":[83],"we":[84,107,171],"propose":[85],"a":[86,109,115,126,145,177,201],"Meta-learning":[87],"based":[88],"Fair":[89],"Ranking":[90],"(MFR)":[91],"model":[92,130],"that":[93,196],"could":[94],"alleviate":[95],"data":[97],"bias":[98],"protected":[100],"through":[102,187],"an":[103,118],"automatically-weighted":[104],"loss.":[105],"Specifically,":[106],"adopt":[108],"meta-learning":[110],"framework":[111],"explicitly":[113],"meta-learner":[116,142],"from":[117],"unbiased":[119],"sampled":[120],"(meta-dataset),":[122],"and":[123,167,181,205],"simultaneously,":[124],"listwise":[127],"learning-to-rank":[128],"(LTR)":[129],"whole":[133],"(biased)":[134],"governed":[136],"by":[137],"\"fair\"":[138],"loss":[139,152],"weights.":[140],"The":[141],"serves":[143],"weighting":[146,165],"function":[147,166],"make":[149],"attend":[153],"more":[154],"update":[160],"parameters":[162],"model,":[170],"formulate":[172],"proposed":[174,198],"MFR":[175],"bilevel":[178],"optimization":[179],"problem":[180],"it":[183],"using":[184],"gradients":[186],"gradients.":[188],"Experimental":[189],"results":[190],"several":[192],"real-world":[193],"datasets":[194],"demonstrate":[195],"method":[199],"achieves":[200],"comparable":[202],"performance":[204],"significantly":[206],"improves":[207],"metric":[210],"compared":[211],"with":[212],"state-of-the-art":[213],"methods.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
