{"id":"https://openalex.org/W4290927705","doi":"https://doi.org/10.1145/3534678.3539055","title":"An Online Multi-task Learning Framework for Google Feed Ads Auction Models","display_name":"An Online Multi-task Learning Framework for Google Feed Ads Auction Models","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290927705","doi":"https://doi.org/10.1145/3534678.3539055"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539055","doi":"https://doi.org/10.1145/3534678.3539055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539055","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539055","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ning Ma","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ning Ma","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043835170","display_name":"Mustafa Ispir","orcid":"https://orcid.org/0009-0009-9309-9516","observed_orcids":["https://orcid.org/0009-0009-9309-9516"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mustafa Ispir","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409799","display_name":"Yuan Li","orcid":"https://orcid.org/0000-0002-2645-7119","observed_orcids":["https://orcid.org/0000-0002-2645-7119"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Li","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101429739","display_name":"Yongpeng Yang","orcid":"https://orcid.org/0000-0003-3992-2814","observed_orcids":["https://orcid.org/0000-0003-3992-2814","https://orcid.org/0000-0002-3181-7211","https://orcid.org/0009-0000-4516-7363"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongpeng Yang","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100457678","display_name":"Zhe Chen","orcid":"https://orcid.org/0000-0002-2919-4481","observed_orcids":["https://orcid.org/0000-0002-2919-4481","https://orcid.org/0009-0005-3379-0613","https://orcid.org/0009-0007-5089-0848","https://orcid.org/0009-0003-7056-0601","https://orcid.org/0009-0004-1735-7097","https://orcid.org/0009-0006-5492-3013","https://orcid.org/0009-0008-3316-5334","https://orcid.org/0000-0003-2611-9635","https://orcid.org/0000-0002-0088-7591"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Chen","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055484401","display_name":"Derek Zhiyuan Cheng","orcid":"https://orcid.org/0009-0000-7943-8328","observed_orcids":["https://orcid.org/0009-0000-7943-8328"]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Derek Zhiyuan Cheng","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067896271","display_name":"Lan Nie","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lan Nie","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015614433","display_name":"Kishor Barman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kishor Barman","raw_affiliation_strings":["Google Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":2.4162,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.90619944,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3477","last_page":"3485"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.8690999746322632,"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":0.8690999746322632,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.048500001430511475,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.020600000396370888,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/real-time-learning","display_name":"real-time learning","score":0.5830000042915344},{"id":"https://openalex.org/keywords/loss-weighting","display_name":"loss weighting","score":0.5289999842643738},{"id":"https://openalex.org/keywords/multi-stage-training","display_name":"multi-stage training","score":0.7910000085830688},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.367000013589859},{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8356157541275024},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7672282457351685},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5497005581855774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5390398502349854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4780981242656708},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.47162842750549316},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4678210914134979},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.43297630548477173},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4236256778240204},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07324790954589844},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539055","doi":"https://doi.org/10.1145/3534678.3539055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539055","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3534678.3539055","doi":"https://doi.org/10.1145/3534678.3539055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539055","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4290927705.pdf","grobid_xml":"https://content.openalex.org/works/W4290927705.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W2074694452","https://openalex.org/W2117130368","https://openalex.org/W2475334473","https://openalex.org/W2732547613","https://openalex.org/W2809290718","https://openalex.org/W2913340405","https://openalex.org/W2963677766","https://openalex.org/W2964182926","https://openalex.org/W2973172293","https://openalex.org/W3034727271","https://openalex.org/W3093945404","https://openalex.org/W4239110337","https://openalex.org/W4365393081"],"related_works":["https://openalex.org/W4327518056","https://openalex.org/W3209519005","https://openalex.org/W7090718799","https://openalex.org/W4213365329","https://openalex.org/W4385208556","https://openalex.org/W4400518819","https://openalex.org/W4207046351","https://openalex.org/W2057376540","https://openalex.org/W2963895309","https://openalex.org/W4225012698"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"introduce":[4],"a":[5],"large":[6],"scale":[7],"online":[8],"multi-task":[9],"deep":[10],"learning":[11,126],"framework":[12],"for":[13,112],"modeling":[14,153],"multiple":[15],"feed":[16,24],"ads":[17,25,45,48],"auction":[18,49],"prediction":[19,29],"tasks":[20,30,74,85],"on":[21,41,64],"an":[22,124],"industry-scale":[23],"recommendation":[26],"platform.":[27],"Multiple":[28],"are":[31],"combined":[32],"into":[33],"one":[34],"single":[35,155],"model":[36,121],"which":[37],"is":[38,97],"continuously":[39],"trained":[40,63],"real":[42],"time":[43,79],"new":[44],"data.":[46],"Multi-tasking":[47],"models":[50],"in":[51],"real-time":[52],"faces":[53],"many":[54],"real-world":[55],"challenges.":[56],"For":[57],"example,":[58],"each":[59,89,95,154],"task":[60,96,156],"may":[61,75],"be":[62],"different":[65,73,77,84,134],"set":[66],"of":[67,72,94,133],"training":[68,111],"data;":[69],"the":[70,92,130],"labels":[71],"have":[76],"arrival":[78],"due":[80],"to":[81,119,128,144,152],"label":[82,114],"delay;":[83],"will":[86],"interact":[87],"with":[88],"other;":[90],"combining":[91],"losses":[93],"non-trivial.":[98],"We":[99,136],"tackle":[100],"these":[101],"challenges":[102],"using":[103],"practical":[104],"and":[105,123,147],"novel":[106],"techniques":[107,141],"such":[108],"as":[109],"multi-stage":[110],"handling":[113],"delay,":[115],"Multi-gate":[116],"Mixture-of-Experts":[117],"(MMoE)":[118],"optimize":[120,129],"interaction":[122],"auto-parameter":[125],"algorithm":[127],"loss":[131],"weights":[132],"tasks.":[135],"demonstrate":[137],"that":[138],"our":[139],"proposed":[140],"can":[142],"lead":[143],"quality":[145],"improvements":[146],"substantial":[148],"resource":[149],"saving":[150],"compared":[151],"independently.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2022-08-13T00:00:00"}
