{"id":"https://openalex.org/W1967533108","doi":"https://doi.org/10.1145/2576230","title":"Learning to Rank from Noisy Data","display_name":"Learning to Rank from Noisy Data","publication_year":2015,"publication_date":"2015-09-29","ids":{"openalex":"https://openalex.org/W1967533108","doi":"https://doi.org/10.1145/2576230","mag":"1967533108"},"language":"en","primary_location":{"id":"doi:10.1145/2576230","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2576230","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-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/A5022926037","display_name":"Wenkui Ding","orcid":"https://orcid.org/0000-0001-7640-6099"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkui Ding","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084564357","display_name":"Xiubo Geng","orcid":"https://orcid.org/0000-0001-6477-7933"},"institutions":[{"id":"https://openalex.org/I1325784139","display_name":"Yahoo (United Kingdom)","ror":"https://ror.org/038p3gq39","country_code":"GB","type":"company","lineage":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"]},{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Xiubo Geng","raw_affiliation_strings":["Yahoo! Labs Beijing","Yahoo&excl; Labs Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo! Labs Beijing","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"Yahoo&excl; Labs Beijing","institution_ids":["https://openalex.org/I1325784139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100441855","display_name":"Xudong Zhang","orcid":"https://orcid.org/0000-0002-6465-7437"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu-Dong Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.8421,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.98899373,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"7","issue":"1","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9961000084877014,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9961000084877014,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9958999752998352,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.8681302666664124},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7687610387802124},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.7405188083648682},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6910839676856995},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.596748948097229},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5932673215866089},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5850367546081543},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5490038394927979},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5368825197219849},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43878379464149475},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.42454153299331665},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3415874242782593},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0891125500202179},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07805415987968445}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8681302666664124},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7687610387802124},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.7405188083648682},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6910839676856995},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.596748948097229},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5932673215866089},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5850367546081543},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5490038394927979},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5368825197219849},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43878379464149475},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42454153299331665},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3415874242782593},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0891125500202179},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07805415987968445},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2576230","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2576230","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W88013650","https://openalex.org/W94637519","https://openalex.org/W349770100","https://openalex.org/W1580256954","https://openalex.org/W1597533204","https://openalex.org/W1660390307","https://openalex.org/W1972594981","https://openalex.org/W1973435495","https://openalex.org/W1985554184","https://openalex.org/W2014415866","https://openalex.org/W2024650720","https://openalex.org/W2030524533","https://openalex.org/W2047221353","https://openalex.org/W2069426496","https://openalex.org/W2069870183","https://openalex.org/W2072112759","https://openalex.org/W2078264666","https://openalex.org/W2100507040","https://openalex.org/W2107189314","https://openalex.org/W2108862644","https://openalex.org/W2109154214","https://openalex.org/W2111557120","https://openalex.org/W2120350143","https://openalex.org/W2120391124","https://openalex.org/W2125398996","https://openalex.org/W2127176025","https://openalex.org/W2128699418","https://openalex.org/W2129245267","https://openalex.org/W2137446405","https://openalex.org/W2143331230","https://openalex.org/W2143806604","https://openalex.org/W2149427297","https://openalex.org/W2151985454","https://openalex.org/W2164641162","https://openalex.org/W2560674852","https://openalex.org/W2884475480","https://openalex.org/W2913668833","https://openalex.org/W3100570787","https://openalex.org/W4251560691","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2293317945","https://openalex.org/W2041353081","https://openalex.org/W2798835721","https://openalex.org/W104148947","https://openalex.org/W3199233695","https://openalex.org/W2352397247","https://openalex.org/W2295417928","https://openalex.org/W2294459391","https://openalex.org/W2237756989","https://openalex.org/W2027289847"],"abstract_inverted_index":{"Learning":[0],"to":[1,28,67,72,87,119,126,130,174,278,291],"rank,":[2],"which":[3,37],"learns":[4],"the":[5,32,48,54,78,82,97,111,115,136,141,156,159,163,176,183,191,200,212,216,220,229,235,248,253,263,303,318,330],"ranking":[6],"function":[7],"from":[8,326],"training":[9,33,83,133,148,164,181,280],"data,":[10],"has":[11],"become":[12],"an":[13],"emerging":[14],"research":[15],"area":[16],"in":[17,81,114,162,199],"information":[18],"retrieval":[19],"and":[20,53,121,168,190,288,309,329],"machine":[21],"learning.":[22],"Most":[23],"existing":[24,128,258],"work":[25],"on":[26,313],"learning":[27,86,118],"rank":[29,88,120],"assumes":[30],"that":[31,155,317],"data":[34,84,165],"is":[35,38,226,231],"clean,":[36],"not":[39],"always":[40],"true,":[41],"however.":[42],"The":[43,197],"ambiguity":[44],"of":[45,50,57,85,99,117,143,158,179,186,194,207,215,223,237,266,285,296],"query":[46],"intent,":[47],"lack":[49],"domain":[51],"knowledge,":[52],"vague":[55],"definition":[56],"relevance":[58,70,79,160,192,217],"levels":[59],"all":[60],"make":[61],"it":[62],"difficult":[63],"for":[64,146,240,305],"common":[65],"annotators":[66],"give":[68,275],"reliable":[69,167],"labels":[71,80,161,193],"some":[73],"documents.":[74,196],"As":[75,299],"a":[76,123,147,171,180,224],"result,":[77],"usually":[89],"contain":[90],"noise.":[91,298],"If":[92,228],"we":[93,108,139,153,169,233,246,256,274,301],"ignore":[94],"this":[95,106,151,241],"fact,":[96],"performance":[98],"learning-to-rank":[100,259,332],"algorithms":[101,129,260,333],"will":[102],"be":[103],"damaged.":[104],"In":[105,135,252],"article,":[107],"propose":[109],"considering":[110],"labeling":[112,144,238,267,286,297],"noise":[113,145,239,268,287],"process":[116,178],"using":[122],"two-step":[124],"approach":[125,320],"extend":[127,257],"handle":[131],"noisy":[132,324],"data.":[134],"first":[137],"step,":[138,255],"estimate":[140],"degree":[142,236,249,265],"document.":[149],"To":[150],"end,":[152],"assume":[154],"majority":[157],"are":[166,203],"use":[170],"graphical":[172,201],"model":[173,202],"describe":[175],"generative":[177],"query,":[182],"feature":[184,221],"vectors":[185],"its":[187],"associated":[188],"documents,":[189],"these":[195],"parameters":[198],"learned":[204],"by":[205,261],"means":[206],"maximum":[208],"likelihood":[209],"estimation.":[210],"Then":[211],"conditional":[213],"probability":[214,230],"label":[218],"given":[219],"vector":[222],"document":[225,242],"computed.":[227],"large,":[232],"regard":[234,247],"as":[243,250],"small;":[244],"otherwise,":[245],"large.":[251],"second":[254],"incorporating":[262],"estimated":[264],"into":[269],"their":[270],"loss":[271],"functions.":[272],"Specifically,":[273],"larger":[276,294],"weights":[277,290],"those":[279,292],"documents":[281,325],"with":[282,293],"smaller":[283,289],"degrees":[284,295],"examples,":[300],"demonstrate":[302],"extensions":[304],"McRank,":[306],"RankSVM,":[307],"RankBoost,":[308],"RankNet.":[310],"Empirical":[311],"results":[312],"benchmark":[314],"datasets":[315],"show":[316],"proposed":[319],"can":[321,334],"effectively":[322],"distinguish":[323],"clean":[327],"ones,":[328],"extended":[331],"achieve":[335],"better":[336],"performances":[337],"than":[338],"baselines.":[339]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":20},{"year":2016,"cited_by_count":7}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
