{"id":"https://openalex.org/W3035511822","doi":"https://doi.org/10.1145/3397271.3401118","title":"Learning to Ask Screening Questions for Job Postings","display_name":"Learning to Ask Screening Questions for Job Postings","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3035511822","doi":"https://doi.org/10.1145/3397271.3401118","mag":"3035511822"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401118","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd 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/A5101424783","display_name":"Baoxu Shi","orcid":"https://orcid.org/0000-0001-7026-5811"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoxu Shi","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324734","display_name":"Shan Li","orcid":"https://orcid.org/0009-0007-2822-2392"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shan Li","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103180391","display_name":"Jaewon Yang","orcid":"https://orcid.org/0009-0001-2224-7915"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaewon Yang","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016971476","display_name":"Mustafa Emre Kazdagli","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mustafa Emre Kazdagli","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090007199","display_name":"Qi He","orcid":"https://orcid.org/0000-0001-5257-6843"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qi He","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"549","last_page":"558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.996999979019165,"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/computer-science","display_name":"Computer science","score":0.7253599166870117},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.6926722526550293},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6703864336013794},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.6316728591918945},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6238057613372803},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5288490056991577},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.5015842914581299},{"id":"https://openalex.org/keywords/workforce","display_name":"Workforce","score":0.4878568649291992},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4647720456123352},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.46105873584747314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4174349904060364},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.34293270111083984},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3380514979362488},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33580291271209717},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10497641563415527},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.1048535704612732},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08666753768920898}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253599166870117},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.6926722526550293},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6703864336013794},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.6316728591918945},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6238057613372803},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5288490056991577},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.5015842914581299},{"id":"https://openalex.org/C2778139618","wikidata":"https://www.wikidata.org/wiki/Q13440398","display_name":"Workforce","level":2,"score":0.4878568649291992},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4647720456123352},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.46105873584747314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4174349904060364},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.34293270111083984},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3380514979362488},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33580291271209717},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10497641563415527},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.1048535704612732},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08666753768920898},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3397271.3401118","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401118","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2051639611","https://openalex.org/W2105621451","https://openalex.org/W2109609717","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2295598076","https://openalex.org/W2427527485","https://openalex.org/W2746385174","https://openalex.org/W2757978590","https://openalex.org/W2798507773","https://openalex.org/W2889670144","https://openalex.org/W2890166583","https://openalex.org/W2893564970","https://openalex.org/W2902360757","https://openalex.org/W2951738332","https://openalex.org/W2962717047","https://openalex.org/W2962902328","https://openalex.org/W2962925243","https://openalex.org/W2964912036","https://openalex.org/W2989031759","https://openalex.org/W2998704965","https://openalex.org/W3099347681","https://openalex.org/W3100612294","https://openalex.org/W3102476541","https://openalex.org/W4236521339","https://openalex.org/W4247780151","https://openalex.org/W4290960952","https://openalex.org/W4385245566","https://openalex.org/W6739901393"],"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/W3207526114"],"abstract_inverted_index":{"At":[0],"LinkedIn,":[1],"we":[2,39,71,96,106,143,182],"want":[3],"to":[4,34,63,78,112,148,166,209,216],"create":[5],"economic":[6],"opportunity":[7],"for":[8,83],"everyone":[9],"in":[10,173],"the":[11,32,90,116,122,158,174,203],"global":[12],"workforce.":[13],"A":[14],"critical":[15],"aspect":[16],"of":[17,92],"this":[18,134],"goal":[19],"is":[20,135],"matching":[21],"jobs":[22,67,218],"with":[23,139,152],"qualified":[24,56,211,221],"applicants.":[25],"To":[26,59,88],"improve":[27],"hiring":[28],"efficiency":[29],"and":[30,119,162,169,196,213],"reduce":[31],"need":[33],"manually":[35],"screening":[36,48,61,81,94,159,185],"each":[37],"applicant,":[38],"develop":[40,97],"a":[41,73,84,98,108,136],"new":[42,74,137],"product":[43,138,161],"where":[44,105],"recruiters":[45,208],"can":[46,54],"ask":[47],"questions":[49,62,82],"online":[50,179],"so":[51],"that":[52,76],"they":[53,219],"filter":[55],"candidates":[57],"easily.":[58],"add":[60],"all":[64],"20M":[65],"active":[66],"at":[68],"Linked":[69],"In,":[70],"propose":[72],"task":[75,91],"aims":[77],"automatically":[79],"generate":[80],"given":[85],"job":[86,175,191,214],"posting.":[87],"solve":[89],"generating":[93],"questions,":[95],"two-stage":[99],"deep":[100,109,145],"learning":[101,110,147],"model":[102,111,206],"called":[103],"Job2Questions,":[104],"apply":[107],"detect":[113],"intent":[114],"from":[115],"text":[117],"description,":[118],"then":[120],"rank":[121],"detected":[123],"intents":[124],"by":[125],"their":[126],"importance":[127],"based":[128],"on":[129],"other":[130],"contextual":[131],"features.":[132],"Since":[133],"no":[140],"historical":[141],"data,":[142],"employ":[144],"transfer":[146],"train":[149],"complex":[150],"models":[151,165],"limited":[153],"training":[154],"data.":[155],"We":[156],"launched":[157],"question":[160,186],"our":[163,178],"AI":[164],"LinkedIn":[167],"users":[168],"observed":[170,183],"significant":[171],"impact":[172],"marketplace.":[176],"During":[177],"A/B":[180],"test,":[181],"+53.10%":[184],"suggestion":[187],"acceptance":[188],"rate,":[189],"+22.17%":[190],"coverage,":[192],"+190%":[193],"recruiter-applicant":[194],"interaction,":[195],"+11":[197],"Net":[198],"Promoter":[199],"Score.":[200],"In":[201],"sum,":[202],"deployed":[204],"Job2Questions":[205],"helps":[207],"find":[210,217],"applicants":[212],"seekers":[215],"are":[220],"for.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}