{"id":"https://openalex.org/W4385261955","doi":"https://doi.org/10.1145/3580305.3599536","title":"Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction","display_name":"Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385261955","doi":"https://doi.org/10.1145/3580305.3599536"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599536","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599536","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599536","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599536","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100398556","display_name":"Yifan Wang","orcid":"https://orcid.org/0000-0002-2933-6363"},"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":true,"raw_author_name":"Yifan Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054609020","display_name":"Peijie Sun","orcid":"https://orcid.org/0000-0001-9733-0521"},"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":"Peijie Sun","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100402996","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0003-3158-1920"},"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":"Min Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032579783","display_name":"Qinglin Jia","orcid":"https://orcid.org/0000-0002-3583-6719"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinglin Jia","raw_affiliation_strings":["Noah's Ark Lab, Huawei, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647029","display_name":"Jingjie Li","orcid":"https://orcid.org/0000-0001-5253-1899"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjie Li","raw_affiliation_strings":["Noah's Ark Lab, Huawei, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100760812","display_name":"Shaoping Ma","orcid":"https://orcid.org/0000-0002-8762-8268"},"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":"Shaoping Ma","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100398556"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.6172,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.93769663,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2456","last_page":"2466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9955000281333923,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9932000041007996,"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":0.7704416513442993},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6920252442359924},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.5773929357528687},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5383023023605347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46245670318603516},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4567490816116333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4486961364746094},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.373294472694397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7704416513442993},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6920252442359924},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.5773929357528687},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5383023023605347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46245670318603516},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4567490816116333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4486961364746094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.373294472694397},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","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/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3580305.3599536","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599536","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599536","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2307.12756","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.12756","pdf_url":"https://arxiv.org/pdf/2307.12756","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"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599536","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599536","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599536","source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2082826544","display_name":null,"funder_award_id":"Postdoctoral","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3379573397","display_name":null,"funder_award_id":"U21B2026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8963711399","display_name":null,"funder_award_id":"2022TQ0178","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385261955.pdf","grobid_xml":"https://content.openalex.org/works/W4385261955.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W2021866613","https://openalex.org/W2723293840","https://openalex.org/W2809290718","https://openalex.org/W2898085636","https://openalex.org/W2946274302","https://openalex.org/W2962989965","https://openalex.org/W2963895309","https://openalex.org/W2973198305","https://openalex.org/W3004726539","https://openalex.org/W3012576969","https://openalex.org/W3035397484","https://openalex.org/W3035466949","https://openalex.org/W3035596828","https://openalex.org/W3088432326","https://openalex.org/W3088502361","https://openalex.org/W3093945404","https://openalex.org/W3100085543","https://openalex.org/W3101704389","https://openalex.org/W3104669598","https://openalex.org/W3110889227","https://openalex.org/W3153549356","https://openalex.org/W3153682915","https://openalex.org/W3153687269","https://openalex.org/W3155850838","https://openalex.org/W3156261048","https://openalex.org/W3167758559","https://openalex.org/W3190524507","https://openalex.org/W4223591050","https://openalex.org/W4224308256","https://openalex.org/W4306317215"],"related_works":["https://openalex.org/W3201448254","https://openalex.org/W4286970243","https://openalex.org/W2066431708","https://openalex.org/W4384133558","https://openalex.org/W3025615835","https://openalex.org/W173210993","https://openalex.org/W2390660599","https://openalex.org/W3028847759","https://openalex.org/W2393688264","https://openalex.org/W3170174360"],"abstract_inverted_index":{"Conversion":[0],"rate":[1],"prediction":[2],"is":[3,43,108,132],"critical":[4],"to":[5,39,52,62,92,134,155],"many":[6],"online":[7],"applications":[8],"such":[9],"as":[10,120],"digital":[11],"display":[12],"advertising.":[13],"To":[14,69],"capture":[15],"dynamic":[16,66],"data":[17,27,63,67,126],"distribution,":[18],"industrial":[19],"systems":[20],"often":[21],"require":[22],"retraining":[23],"models":[24],"on":[25,160],"recent":[26],"daily":[28],"or":[29],"weekly.":[30],"However,":[31],"the":[32,54,72,105,113,176,183],"delay":[33],"of":[34,112],"conversion":[35],"behavior":[36],"usually":[37],"leads":[38],"incorrect":[40],"labeling,":[41],"which":[42,87],"called":[44],"delayed":[45,81,177],"feedback":[46,74,82,98,178],"problem.":[47],"Existing":[48],"work":[49],"may":[50],"fail":[51],"introduce":[53,71],"correct":[55,73,93],"information":[56],"about":[57],"false":[58],"negative":[59,97],"samples":[60],"due":[61],"sparsity":[64],"and":[65,163,166,180],"distribution.":[68],"directly":[70],"label":[75,128],"information,":[76],"we":[77,101,148],"propose":[78],"an":[79,89,109,150],"Unbiased":[80],"Label":[83],"Correction":[84],"framework":[85],"(ULC),":[86],"uses":[88],"auxiliary":[90],"model":[91],"labels":[94],"for":[95,127],"observed":[96],"samples.":[99],"Firstly,":[100],"theoretically":[102],"prove":[103],"that":[104,170],"label-corrected":[106],"loss":[107,115],"unbiased":[110],"estimate":[111],"oracle":[114],"using":[116],"true":[117],"labels.":[118],"Then,":[119],"there":[121],"are":[122],"no":[123],"ready":[124],"training":[125,137,146,153],"correction,":[129],"counterfactual":[130,141],"labeling":[131,142],"used":[133],"construct":[135],"artificial":[136],"data.":[138],"Furthermore,":[139],"since":[140],"utilizes":[143],"only":[144],"partial":[145],"data,":[147],"design":[149],"embedding-based":[151],"alternative":[152],"method":[154],"enhance":[156],"performance.":[157],"Comparative":[158],"experiments":[159],"both":[161],"public":[162],"private":[164],"datasets":[165],"detailed":[167],"analyses":[168],"show":[169],"our":[171],"proposed":[172],"approach":[173],"effectively":[174],"alleviates":[175],"problem":[179],"consistently":[181],"outperforms":[182],"previous":[184],"state-of-the-art":[185],"methods.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}