{"id":"https://openalex.org/W2987016235","doi":"https://doi.org/10.1145/3357384.3357936","title":"Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction","display_name":"Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2987016235","doi":"https://doi.org/10.1145/3357384.3357936","mag":"2987016235"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3357936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.00886","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yikai Wang","orcid":null},"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":"Yikai 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":null,"display_name":"Liang Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Zhang","raw_affiliation_strings":["JD.COM, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.COM, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Quanyu Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Quanyu Dai","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fuchun Sun","orcid":null},"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":"Fuchun 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":null,"display_name":"Bo Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["JD.COM, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.COM, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yang He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang He","raw_affiliation_strings":["JD.COM, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.COM, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Weipeng Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weipeng Yan","raw_affiliation_strings":["JD.COM, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.COM, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yongjun Bao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Bao","raw_affiliation_strings":["JD.COM, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD.COM, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.1666,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93261434,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"349","last_page":"358"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9977999925613403,"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/T11478","display_name":"Caching and Content Delivery","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8075000047683716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6703000068664551},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.656000018119812},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5266000032424927},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4043999910354614},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3797000050544739},{"id":"https://openalex.org/keywords/neglect","display_name":"Neglect","score":0.35269999504089355}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8075000047683716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.708299994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6956999897956848},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6703000068664551},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.656000018119812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5552999973297119},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C2776289891","wikidata":"https://www.wikidata.org/wiki/Q1931511","display_name":"Neglect","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.26989999413490295}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3357384.3357936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3357936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1911.00886","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.00886","pdf_url":"https://arxiv.org/pdf/1911.00886","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":"pmh:oai:arXiv.org:1911.00886","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.00886","pdf_url":"https://arxiv.org/pdf/1911.00886","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1949314668","https://openalex.org/W2158698691","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2619206542","https://openalex.org/W2723293840","https://openalex.org/W2750004028","https://openalex.org/W2809291355","https://openalex.org/W2950491307","https://openalex.org/W2962745591","https://openalex.org/W6669934560"],"related_works":[],"abstract_inverted_index":{"Improving":[0],"the":[1,11,19,56,59,82,92,110,121,126,137,145],"performance":[2],"of":[3,10,21,58,113,120],"click-through":[4],"rate":[5],"(CTR)":[6],"prediction":[7,25,53],"remains":[8],"one":[9],"core":[12],"tasks":[13],"in":[14],"online":[15],"advertising":[16,162],"systems.":[17],"With":[18],"rise":[20],"deep":[22,28,35],"learning,":[23],"CTR":[24,36,52,114,129,155],"models":[26],"with":[27,144],"networks":[29],"remarkably":[30],"enhance":[31],"model":[32,73,148],"capacities.":[33],"In":[34],"models,":[37],"exploiting":[38],"users'":[39,46,64,83],"historical":[40,65],"data":[41,115],"is":[42],"essential":[43],"for":[44,80,90,105],"learning":[45],"behaviors":[47,85],"and":[48,86,116,140,160],"interests.":[49],"As":[50],"existing":[51],"works":[54],"neglect":[55],"importance":[57],"temporal":[60,78,88,93],"signals":[61,79,89],"when":[62],"embed":[63],"clicking":[66],"records,":[67],"we":[68,98],"propose":[69,99],"a":[70,100],"time-aware":[71,146],"attention":[72,147],"which":[74,108],"explicitly":[75],"uses":[76],"absolute":[77],"expressing":[81,91],"periodic":[84],"relative":[87],"relation":[94],"between":[95],"items.":[96],"Besides,":[97],"regularized":[101],"adversarial":[102,132],"sampling":[103,107,133],"strategy":[104,134],"negative":[106,128],"eases":[109],"classification":[111],"imbalance":[112],"can":[117,141],"make":[118],"use":[119],"strong":[122],"guidance":[123],"provided":[124],"by":[125],"observed":[127],"samples.":[130],"The":[131],"significantly":[135],"improves":[136],"training":[138],"efficiency,":[139],"be":[142],"co-trained":[143],"seamlessly.":[149],"Experiments":[150],"are":[151],"conducted":[152],"on":[153],"real-world":[154],"datasets":[156],"from":[157],"both":[158],"in-station":[159],"out-station":[161],"places.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-11-22T00:00:00"}