{"id":"https://openalex.org/W3091792641","doi":"https://doi.org/10.1145/3437963.3441705","title":"Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks","display_name":"Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks","publication_year":2021,"publication_date":"2021-03-06","ids":{"openalex":"https://openalex.org/W3091792641","doi":"https://doi.org/10.1145/3437963.3441705","mag":"3091792641"},"language":"en","primary_location":{"id":"doi:10.1145/3437963.3441705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2010.02419","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065359790","display_name":"Daniel Nemirovsky","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daniel Nemirovsky","raw_affiliation_strings":["Hired Inc., San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Hired Inc., San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039471127","display_name":"Nicolas Thi\u00e9baut","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolas Thiebaut","raw_affiliation_strings":["Hired Inc., San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Hired Inc., San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044903040","display_name":"Ye Xu","orcid":"https://orcid.org/0000-0002-0372-7630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye Xu","raw_affiliation_strings":["Hired Inc., San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Hired Inc., San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100727390","display_name":"Abhishek Gupta","orcid":"https://orcid.org/0000-0002-2057-826X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abhishek Gupta","raw_affiliation_strings":["Hired Inc., San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Hired Inc., San Francisco, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065359790"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0969,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.33992966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1089","last_page":"1092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9995999932289124,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9975000023841858,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9915000200271606,"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/adversarial-system","display_name":"Adversarial system","score":0.9026306867599487},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7511106729507446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7132799029350281},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5591055154800415},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.529466986656189},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.46470609307289124},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4304201900959015},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.41670161485671997},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3814258873462677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31236323714256287},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.23053786158561707},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11370483040809631},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.05683746933937073}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9026306867599487},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7511106729507446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132799029350281},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5591055154800415},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.529466986656189},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.46470609307289124},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4304201900959015},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.41670161485671997},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3814258873462677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31236323714256287},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.23053786158561707},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11370483040809631},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.05683746933937073},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3437963.3441705","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437963.3441705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2010.02419","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.02419","pdf_url":"https://arxiv.org/pdf/2010.02419","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3091792641","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2010.02419.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2010.02419","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2010.02419","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2010.02419","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.02419","pdf_url":"https://arxiv.org/pdf/2010.02419","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3091792641.pdf","grobid_xml":"https://content.openalex.org/works/W3091792641.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W2099471712","https://openalex.org/W2122538988","https://openalex.org/W2953945697","https://openalex.org/W2963125461","https://openalex.org/W3085152052","https://openalex.org/W3103557498","https://openalex.org/W3103795814"],"related_works":["https://openalex.org/W3133709975","https://openalex.org/W2397110394","https://openalex.org/W1762092152","https://openalex.org/W1771806187","https://openalex.org/W2136222780","https://openalex.org/W2792991502","https://openalex.org/W2097569345","https://openalex.org/W3127979761","https://openalex.org/W3111892720","https://openalex.org/W2711140174","https://openalex.org/W2293759700","https://openalex.org/W2754215704","https://openalex.org/W2150638405","https://openalex.org/W2183488048","https://openalex.org/W2240173141","https://openalex.org/W52538035","https://openalex.org/W2738768261","https://openalex.org/W2048243901","https://openalex.org/W3095312497","https://openalex.org/W3175186725"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"predictors":[2],"have":[3,57],"been":[4,58],"increasingly":[5],"applied":[6],"in":[7,11,45,60,95,99,137],"production":[8,100],"settings,":[9],"including":[10],"one":[12],"of":[13,42,62,109,134],"the":[14,46,106,131],"world's":[15],"largest":[16],"hiring":[17],"platforms,":[18],"Hired,":[19],"to":[20,30,38,87,97,118],"provide":[21,31,92],"a":[22,74,111],"better":[23],"candidate":[24,142],"and":[25,64,91],"recruiter":[26],"experience.":[27],"The":[28],"ability":[29],"actionable":[32,55,93],"feedback":[33,56,94],"is":[34],"desirable":[35],"for":[36],"candidates":[37,98],"improve":[39],"their":[40],"chances":[41],"achieving":[43],"success":[44],"marketplace.":[47],"Until":[48],"recently,":[49],"however,":[50],"methods":[51],"aimed":[52],"at":[53],"providing":[54],"limited":[59],"terms":[61],"realism":[63],"latency.":[65],"In":[66],"this":[67,135],"work,":[68],"we":[69,84],"demonstrate":[70],"how,":[71],"by":[72],"applying":[73],"newly":[75],"introduced":[76],"method":[77],"based":[78],"on":[79,114,139],"Generative":[80],"Adversarial":[81],"Networks":[82],"(GANs),":[83],"are":[85],"able":[86],"overcome":[88],"these":[89],"limitations":[90],"real-time":[96],"settings.":[101],"Our":[102],"experimental":[103],"results":[104],"highlight":[105],"significant":[107],"benefits":[108],"utilizing":[110],"GAN-based":[112],"approach":[113,136],"our":[115],"dataset":[116],"relative":[117],"two":[119,140],"other":[120],"state-of-the-art":[121],"approaches":[122],"(including":[123],"over":[124],"1000x":[125],"latency":[126],"gains).":[127],"We":[128],"also":[129],"illustrate":[130],"potential":[132],"impact":[133],"detail":[138],"real":[141],"profile":[143],"examples.":[144]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-21T08:13:44.787528","created_date":"2022-07-25T00:00:00"}