{"id":"https://openalex.org/W3030761159","doi":"https://doi.org/10.1145/3383972.3383991","title":"An Attention-based Deep Network for CTR Prediction","display_name":"An Attention-based Deep Network for CTR Prediction","publication_year":2020,"publication_date":"2020-02-15","ids":{"openalex":"https://openalex.org/W3030761159","doi":"https://doi.org/10.1145/3383972.3383991","mag":"3030761159"},"language":"en","primary_location":{"id":"doi:10.1145/3383972.3383991","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3383991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","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/A5100377413","display_name":"Hailong Zhang","orcid":"https://orcid.org/0000-0002-9639-8345"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hailong Zhang","raw_affiliation_strings":["Communication University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077266611","display_name":"Jinyao Yan","orcid":"https://orcid.org/0000-0003-4153-313X"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyao Yan","raw_affiliation_strings":["Communication University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101663754","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0001-7591-3996"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zhang","raw_affiliation_strings":["Communication University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100377413"],"corresponding_institution_ids":["https://openalex.org/I75689368"],"apc_list":null,"apc_paid":null,"fwci":1.0676,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.83259539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8575389981269836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6802054047584534},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6383171081542969},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6332405805587769},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6322648525238037},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5689601302146912},{"id":"https://openalex.org/keywords/click-through-rate","display_name":"Click-through rate","score":0.5337002873420715},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4767892360687256},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4733697772026062},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4716569483280182},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45457834005355835},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4420850872993469},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43416374921798706},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42749685049057007},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.41568344831466675},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.3269054889678955},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.14950820803642273},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08249318599700928},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08058711886405945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8575389981269836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6802054047584534},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6383171081542969},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6332405805587769},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6322648525238037},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5689601302146912},{"id":"https://openalex.org/C115174607","wikidata":"https://www.wikidata.org/wiki/Q1100934","display_name":"Click-through rate","level":2,"score":0.5337002873420715},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4767892360687256},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4733697772026062},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4716569483280182},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45457834005355835},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4420850872993469},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43416374921798706},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42749685049057007},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.41568344831466675},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3269054889678955},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.14950820803642273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08249318599700928},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08058711886405945},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3383972.3383991","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3383991","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G8731146463","display_name":null,"funder_award_id":"61971382","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W1985759455","https://openalex.org/W2055079831","https://openalex.org/W2064675550","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2131876387","https://openalex.org/W2133564696","https://openalex.org/W2157331557","https://openalex.org/W2163605009","https://openalex.org/W2414906602","https://openalex.org/W2443960221","https://openalex.org/W2475334473","https://openalex.org/W2583075099","https://openalex.org/W2912500072","https://openalex.org/W6666761814","https://openalex.org/W6679436768","https://openalex.org/W6697546013","https://openalex.org/W6718631636","https://openalex.org/W6833262898"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,85,119],"is":[4],"a":[5,82],"crucial":[6],"topic":[7],"in":[8],"online":[9],"advertising":[10],"system.":[11],"Early":[12],"researchers":[13,41],"proposed":[14,114],"numerous":[15],"shallow":[16],"models":[17,66],"to":[18,35],"analyze":[19],"this":[20,77],"issue,":[21],"such":[22,46],"as":[23,47],"logistic":[24],"regression,":[25],"factorization":[26],"machines":[27],"and":[28,70,91],"Gradient":[29],"boosting":[30],"decision":[31],"tree.":[32],"In":[33],"order":[34],"improve":[36,117],"the":[37,48,79,99,118,123],"model":[38,86,115],"performance":[39,120],"furthermore,":[40],"propose":[42],"some":[43],"deep":[44,83],"models,":[45,112],"factorization-machine":[49],"supported":[50],"neural":[51],"networks,":[52],"Wide&Deep,":[53],"DeepFM.":[54],"Normally,":[55],"user's":[56],"historical":[57,73,101,129],"behavior":[58],"data":[59],"contains":[60],"abundant":[61],"feature":[62],"information,":[63],"but":[64],"above":[65],"lack":[67],"of":[68,72,98],"modeling":[69],"analysis":[71],"data.":[74],"To":[75],"address":[76],"problem,":[78],"paper":[80],"proposes":[81],"CTR":[84],"based":[87],"on":[88],"attention":[89],"mechanism":[90],"GRU":[92],"model,":[93],"which":[94],"can":[95,116],"make":[96],"use":[97],"users'":[100],"behaviors.":[102,130],"The":[103],"experimental":[104],"results":[105],"demonstrate":[106],"that":[107],"compared":[108],"with":[109],"other":[110],"popular":[111],"our":[113],"by":[121],"extracting":[122],"implied":[124],"interest":[125],"features":[126],"from":[127],"user":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}
