{"id":"https://openalex.org/W4396722732","doi":"https://doi.org/10.1145/3589334.3645379","title":"Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate Estimation","display_name":"Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate Estimation","publication_year":2024,"publication_date":"2024-05-08","ids":{"openalex":"https://openalex.org/W4396722732","doi":"https://doi.org/10.1145/3589334.3645379"},"language":"en","primary_location":{"id":"doi:10.1145/3589334.3645379","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589334.3645379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2024","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/A5100338394","display_name":"Xinyue Zhang","orcid":"https://orcid.org/0000-0002-9456-0994"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyue Zhang","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022686298","display_name":"Cong Huang","orcid":"https://orcid.org/0009-0008-1111-8158"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Huang","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021428475","display_name":"Kaihan Zheng","orcid":"https://orcid.org/0009-0002-2930-8896"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Zheng","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101588702","display_name":"Hongzu Su","orcid":"https://orcid.org/0000-0002-1464-6764"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongzu Su","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101952960","display_name":"Tianxu Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxu Ji","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046451721","display_name":"Wei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078938296","display_name":"H.H. Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongkai Qi","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338386","display_name":"Jingjing Li","orcid":"https://orcid.org/0000-0002-5504-2529"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Li","raw_affiliation_strings":["Tencent, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tencent, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100338394"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":1.4548,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83812939,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3287","last_page":"3296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9818000197410583,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9818000197410583,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9670000076293945,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"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/T11165","display_name":"Image and Video Quality Assessment","score":0.960099995136261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/debiasing","display_name":"Debiasing","score":0.9622371196746826},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.76220703125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6958814263343811},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.6763205528259277},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6463788747787476},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38670262694358826},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1042792797088623},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06546139717102051}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9622371196746826},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.76220703125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958814263343811},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.6763205528259277},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6463788747787476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38670262694358826},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1042792797088623},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06546139717102051},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589334.3645379","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3589334.3645379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1493730910","https://openalex.org/W2158698691","https://openalex.org/W2187089797","https://openalex.org/W2593768305","https://openalex.org/W2765811365","https://openalex.org/W2768307941","https://openalex.org/W2798283910","https://openalex.org/W2962989965","https://openalex.org/W3012576969","https://openalex.org/W3035397484","https://openalex.org/W3035596828","https://openalex.org/W3083370850","https://openalex.org/W3088432326","https://openalex.org/W3092103025","https://openalex.org/W3153682915","https://openalex.org/W3199916614","https://openalex.org/W3205799947","https://openalex.org/W4223591050","https://openalex.org/W4224325518","https://openalex.org/W4297969478","https://openalex.org/W4304080478","https://openalex.org/W4306317202","https://openalex.org/W4307008438","https://openalex.org/W4313410623","https://openalex.org/W4382239957","https://openalex.org/W4382240101","https://openalex.org/W4387846403","https://openalex.org/W6600291067","https://openalex.org/W6637618735","https://openalex.org/W6683445055"],"related_works":["https://openalex.org/W4362554880","https://openalex.org/W4281684980","https://openalex.org/W4386875279","https://openalex.org/W2171721708","https://openalex.org/W4390963114","https://openalex.org/W4287887864","https://openalex.org/W4221165959","https://openalex.org/W4225810998","https://openalex.org/W4280601492","https://openalex.org/W4377864593"],"abstract_inverted_index":{"In":[0,48],"real-world":[1,210],"industrial":[2,229],"scenarios,":[3],"post-click":[4],"conversion":[5],"rate":[6],"(CVR)":[7],"prediction":[8,46,53,94],"models":[9],"are":[10,30],"trained":[11],"offline":[12],"based":[13],"on":[14,182,202],"click":[15,188],"events":[16,29],"and":[17,24,109,115,130,170,208],"subsequently":[18],"applied":[19],"online":[20],"to":[21,33,36,41,50,124,174],"both":[22,166,203],"clicked":[23],"unclicked":[25,28,55],"events.":[26],"Unfortunately,":[27],"inevitably":[31],"difficult":[32],"estimate":[34,51],"due":[35],"user":[37],"self-selection,":[38],"which":[39,163],"leads":[40],"a":[42,89,103,118,126,145,158,227],"degradation":[43],"of":[44,54,67,83,106,140,179,193,219],"CVR":[45,84,93],"accuracy.":[47,95],"order":[49],"the":[52,57,65,77,80,92,107,133,138,141,167,171,176,183,187,191,204,209,217,239],"events,":[56],"current":[58],"mainstream":[59],"Doubly":[60],"Robust":[61],"(DR)":[62],"estimators":[63,114],"introduce":[64,117],"concept":[66],"imputed":[68,73],"errors.":[69],"However,":[70],"inaccuracies":[71],"in":[72,79,88,91,112,144,238],"errors":[74],"can":[75],"increase":[76],"uncertainty":[78],"generalization":[81],"bound":[82],"predictions,":[85],"consequently":[86],"resulting":[87],"decline":[90],"To":[96],"challenge":[97],"this":[98],"issue,":[99],"we":[100,156,224],"first":[101],"present":[102],"theoretical":[104],"analysis":[105],"bias":[108,129,236],"variance":[110,131],"inherent":[111],"DR":[113,134],"then":[116],"novel":[119,159],"causal":[120,177],"estimator":[121],"that":[122],"seeks":[123],"strike":[125],"balance":[127],"between":[128],"within":[132],"framework,":[135],"thus":[136],"optimizing":[137],"learning":[139,154],"imputation":[142],"model":[143],"more":[146],"robust":[147],"manner.":[148],"Additionally,":[149],"drawing":[150],"inspiration":[151],"from":[152,165],"adversarial":[153,161],"techniques,":[155],"propose":[157],"dual":[160],"component,":[162],"learns":[164],"space":[168],"level":[169,173],"task":[172,185],"eliminate":[175],"influence":[178],"input":[180],"features":[181],"CTR":[184],"(i.e.,":[186],"propensity),":[189],"with":[190],"goal":[192],"achieving":[194],"unbiased":[195],"estimations.":[196],"Our":[197],"extensive":[198],"experimental":[199],"evaluations,":[200],"conducted":[201],"widely":[205],"used":[206,233],"benchmark":[207],"large-scale":[211],"Internet":[212],"giant":[213],"platform,":[214],"convincingly":[215],"demonstrate":[216],"effectiveness":[218],"our":[220],"proposed":[221],"scheme.":[222],"Besides,":[223],"have":[225],"released":[226],"high-quality":[228],"dataset":[230],"named":[231],"Tenc-UnionAds":[232],"for":[234],"selection":[235],"research":[237],"advertising":[240],"field.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}