{"id":"https://openalex.org/W2000244138","doi":"https://doi.org/10.1145/2611040.2611074","title":"Aggregation of Crowdsourced Labels Based on Worker History","display_name":"Aggregation of Crowdsourced Labels Based on Worker History","publication_year":2014,"publication_date":"2014-05-27","ids":{"openalex":"https://openalex.org/W2000244138","doi":"https://doi.org/10.1145/2611040.2611074","mag":"2000244138"},"language":"en","primary_location":{"id":"doi:10.1145/2611040.2611074","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2611040.2611074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)","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/A5057095068","display_name":"Mihai G\u00e9orgescu","orcid":"https://orcid.org/0000-0003-1601-0580"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]},{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Mihai Georgescu","raw_affiliation_strings":["L3S Research Center, Leibniz Universit\u00e4t Hannover, Appelstr. 9a, 30167 Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"L3S Research Center, Leibniz Universit\u00e4t Hannover, Appelstr. 9a, 30167 Hannover, Germany","institution_ids":["https://openalex.org/I4210136150","https://openalex.org/I114112103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021268896","display_name":"Xiaofei Zhu","orcid":"https://orcid.org/0000-0001-8239-7176"},"institutions":[{"id":"https://openalex.org/I114112103","display_name":"Leibniz University Hannover","ror":"https://ror.org/0304hq317","country_code":"DE","type":"education","lineage":["https://openalex.org/I114112103"]},{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Xiaofei Zhu","raw_affiliation_strings":["L3S Research Center, Leibniz Universit\u00e4t Hannover, Appelstr. 9a, 30167 Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"L3S Research Center, Leibniz Universit\u00e4t Hannover, Appelstr. 9a, 30167 Hannover, Germany","institution_ids":["https://openalex.org/I4210136150","https://openalex.org/I114112103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057095068"],"corresponding_institution_ids":["https://openalex.org/I114112103","https://openalex.org/I4210136150"],"apc_list":null,"apc_paid":null,"fwci":3.6098,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93176243,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"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":1.0,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9800999760627747,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9692000150680542,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.9723643064498901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7858463525772095},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6832370758056641},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.638157069683075},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5997775197029114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5458659529685974},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4831525683403015},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45287829637527466},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3590245842933655},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08758556842803955},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08673852682113647}],"concepts":[{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.9723643064498901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7858463525772095},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6832370758056641},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.638157069683075},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5997775197029114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5458659529685974},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4831525683403015},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45287829637527466},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3590245842933655},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08758556842803955},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08673852682113647},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2611040.2611074","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2611040.2611074","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W9014458","https://openalex.org/W1497983610","https://openalex.org/W1543648998","https://openalex.org/W1938221098","https://openalex.org/W1970381522","https://openalex.org/W1979483312","https://openalex.org/W2006471615","https://openalex.org/W2022363284","https://openalex.org/W2044945272","https://openalex.org/W2050867684","https://openalex.org/W2055601365","https://openalex.org/W2056997037","https://openalex.org/W2066640191","https://openalex.org/W2096519149","https://openalex.org/W2098865355","https://openalex.org/W2107756083","https://openalex.org/W2108216421","https://openalex.org/W2113878109","https://openalex.org/W2125943921","https://openalex.org/W2131565021","https://openalex.org/W2134305421","https://openalex.org/W2142518823","https://openalex.org/W2145492473","https://openalex.org/W2149273804","https://openalex.org/W2168144930","https://openalex.org/W2201853392","https://openalex.org/W2221430606","https://openalex.org/W2288846786","https://openalex.org/W2296706562","https://openalex.org/W2408116376","https://openalex.org/W2408133538","https://openalex.org/W2611973430","https://openalex.org/W2913879771","https://openalex.org/W3139506240","https://openalex.org/W6682171051"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W4384486036","https://openalex.org/W135177976","https://openalex.org/W1503094549","https://openalex.org/W2337920774","https://openalex.org/W2886410948","https://openalex.org/W2025875869","https://openalex.org/W4318823662","https://openalex.org/W3207526114","https://openalex.org/W4286908577"],"abstract_inverted_index":{"Using":[0],"crowdsourcing":[1],"for":[2,8,27,83,144],"gathering":[3,65,209],"labels":[4,39,120,170],"can":[5,121,194],"be":[6,51,122,195],"beneficial":[7],"supervised":[9],"machine":[10],"learning,":[11],"if":[12],"done":[13],"in":[14,53,118],"the":[15,29,36,92,98,102,110,114,127,135,138,163,183,218,222],"right":[16],"way.":[17],"Crowdsourcing":[18],"is":[19,62,131],"more":[20,66],"cost-effective":[21],"and":[22,95,166,190,207],"faster":[23],"than":[24,67],"employing":[25],"experts":[26],"labeling":[28],"items":[30],"needed":[31],"as":[32,157],"training":[33],"examples.":[34],"Unfortunately,":[35],"crowd":[37,129],"produced":[38],"are":[40],"not":[41,177],"always":[42],"of":[43,60,126,137,160,217,224],"a":[44,80,142,146,154],"comparable":[45],"quality.":[46,58],"Therefore,":[47],"different":[48,73,205],"methods":[49],"could":[50],"employed":[52],"order":[54],"to":[55,106],"assure":[56],"label":[57,69,130,156,208],"One":[59],"them":[61],"redundancy,":[63],"by":[64,134],"one":[68],"per":[70],"item,":[71],"from":[72],"assessors.":[74],"In":[75],"this":[76],"paper":[77],"we":[78,116,151,173,212],"introduce":[79],"novel":[81],"method":[82,143,200],"aggregating":[84],"multiple":[85,202],"crowdsourced":[86],"binary":[87],"labels,":[88],"taking":[89],"into":[90],"account":[91],"worker's":[93],"history":[94],"how":[96,161,167],"well":[97],"worker":[99,111,139,178],"agrees":[100],"with":[101],"aggregated":[103,128,149],"label.":[104],"According":[105],"previously":[107],"solved":[108],"tasks,":[109],"expertise,":[112],"or":[113,176],"confidence":[115,179],"have":[117],"his":[119],"assessed.":[123],"The":[124],"computation":[125],"mutually":[132],"reinforced":[133],"assessment":[136],"confidence.":[140],"Besides":[141],"computing":[145],"hard":[147],"nominal":[148],"label,":[150,185],"also":[152],"propose":[153],"soft":[155],"an":[158],"indicator":[159],"much":[162],"labelers":[164],"agree":[165],"strong":[168],"their":[169],"are.":[171],"Furthermore,":[172],"investigate":[174],"whether":[175,186],"should":[180],"depend":[181],"on":[182,201],"provided":[184],"discriminating":[187],"between":[188],"positive":[189],"negative":[191],"answer":[192],"quality":[193],"beneficial.":[196],"We":[197],"evaluate":[198],"our":[199,225],"datasets,":[203],"covering":[204],"domains":[206],"strategies.":[210],"Moreover,":[211],"compare":[213],"against":[214],"other":[215],"state":[216],"art":[219],"methods,":[220],"showing":[221],"effectiveness":[223],"proposed":[226],"approach.":[227]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":5},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}