{"id":"https://openalex.org/W2162815002","doi":"https://doi.org/10.1145/2339530.2339571","title":"Learning from crowds in the presence of schools of thought","display_name":"Learning from crowds in the presence of schools of thought","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2162815002","doi":"https://doi.org/10.1145/2339530.2339571","mag":"2162815002"},"language":"en","primary_location":{"id":"doi:10.1145/2339530.2339571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5106668475","display_name":"Yuandong Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuandong Tian","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100606995","display_name":"Jun Zhu","orcid":"https://orcid.org/0000-0002-6254-2388"},"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":"Jun Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.9992,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.98358638,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"226","last_page":"234"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9868000149726868,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/clarity","display_name":"CLARITY","score":0.8024305105209351},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7229390144348145},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.7228725552558899},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6950547099113464},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6182716488838196},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6017357110977173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5837565660476685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5178806781768799},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.49093082547187805},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.4674692153930664},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.4340447187423706},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13774707913398743},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12594562768936157}],"concepts":[{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.8024305105209351},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7229390144348145},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7228725552558899},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6950547099113464},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6182716488838196},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6017357110977173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5837565660476685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5178806781768799},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.49093082547187805},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.4674692153930664},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4340447187423706},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13774707913398743},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12594562768936157},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2339530.2339571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2339530.2339571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W9014458","https://openalex.org/W1967687583","https://openalex.org/W1970381522","https://openalex.org/W2001082470","https://openalex.org/W2066459332","https://openalex.org/W2080972498","https://openalex.org/W2089484716","https://openalex.org/W2098865355","https://openalex.org/W2099537822","https://openalex.org/W2114269021","https://openalex.org/W2115870554","https://openalex.org/W2130428211","https://openalex.org/W2134305421","https://openalex.org/W2135880665","https://openalex.org/W2137935418","https://openalex.org/W2140890285","https://openalex.org/W2142518823","https://openalex.org/W2144660879","https://openalex.org/W2145612656","https://openalex.org/W2149273804","https://openalex.org/W2151401338","https://openalex.org/W2154096410","https://openalex.org/W2165874743","https://openalex.org/W2181600409","https://openalex.org/W6679959949"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W4384486036","https://openalex.org/W1503094549","https://openalex.org/W2337920774","https://openalex.org/W4286908577","https://openalex.org/W2886410948","https://openalex.org/W2025875869","https://openalex.org/W4318823662","https://openalex.org/W2086338133"],"abstract_inverted_index":{"Crowdsourcing":[0],"has":[1],"recently":[2],"become":[3],"popular":[4],"among":[5],"machine":[6],"learning":[7],"researchers":[8],"and":[9,67,82,117,162,202,205],"social":[10],"scientists":[11],"as":[12,45,47],"an":[13,154,166],"effective":[14],"way":[15],"to":[16,35,41,113,122,137,168,208,219],"collect":[17],"large-scale":[18],"experimental":[19],"data":[20,183],"from":[21,28,185],"distributed":[22],"workers.":[23],"To":[24],"extract":[25],"useful":[26],"information":[27],"the":[29,72,123,134,171,192,221],"cheap":[30],"but":[31],"potentially":[32],"unreliable":[33],"answers":[34,86],"tasks,":[36],"a":[37,93,110,143,147],"key":[38],"problem":[39],"is":[40,129,206],"identify":[42],"reliable":[43],"workers":[44,88],"well":[46],"unambiguous":[48],"tasks.":[49,227],"Although":[50],"for":[51,77,225],"objective":[52,226],"tasks":[53,78],"that":[54,79,87],"have":[55],"one":[56],"correct":[57],"answer":[58],"per":[59],"task,":[60],"previous":[61],"works":[62],"can":[63,159,216],"estimate":[64,114],"worker":[65,115,200,214],"reliability":[66,116,201,215],"task":[68,118,203],"clarity":[69,119],"based":[70],"on":[71,181],"single":[73,124],"gold":[74,125,222],"standard":[75,126,223],"assumption,":[76],"are":[80],"subjective":[81],"accept":[83],"multiple":[84],"reasonable":[85,197],"may":[89],"be":[90,102,160,217],"grouped":[91],"into,":[92],"phenomenon":[94],"called":[95],"schools":[96,139,193],"of":[97,140,146,152,173,194,199],"thought,":[98,195],"existing":[99],"models":[100],"cannot":[101],"trivially":[103],"applied.":[104],"In":[105],"this":[106],"work,":[107],"we":[108,164],"present":[109,165],"statistical":[111],"model":[112],"without":[120],"resorting":[121],"assumption.":[127],"This":[128],"instantiated":[130],"by":[131],"explicitly":[132],"characterizing":[133],"grouping":[135],"behavior":[136],"form":[138],"thought":[141],"with":[142],"rank-1":[144],"factorization":[145],"worker-task":[148],"groupsize":[149],"matrix.":[150],"Instead":[151],"performing":[153],"intermediate":[155],"inference":[156],"step,":[157],"which":[158],"expensive":[161],"unstable,":[163],"algorithm":[167],"analytically":[169],"compute":[170],"sizes":[172],"different":[174],"groups.":[175],"We":[176],"perform":[177],"extensive":[178],"empirical":[179],"studies":[180],"real":[182],"collected":[184],"Amazon":[186],"Mechanical":[187],"Turk.":[188],"Our":[189],"method":[190],"discovers":[191],"shows":[196],"estimation":[198],"clarity,":[204],"robust":[207],"hyperparameter":[209],"changes.":[210],"Furthermore,":[211],"our":[212],"estimated":[213],"used":[218],"improve":[220],"prediction":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":11},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
