{"id":"https://openalex.org/W3155541141","doi":"https://doi.org/10.1145/3404835.3462936","title":"Looking at CTR Prediction Again: Is Attention All You Need?","display_name":"Looking at CTR Prediction Again: Is Attention All You Need?","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3155541141","doi":"https://doi.org/10.1145/3404835.3462936","mag":"3155541141"},"language":"en","primary_location":{"id":"doi:10.1145/3404835.3462936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404835.3462936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.05563","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuan Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210157345","display_name":"Venus Medtech (China)","ror":"https://ror.org/05xzt2h26","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210157345"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Cheng","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, Beijing, China","institution_ids":["https://openalex.org/I4210157345"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yanbo Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I4210157345","display_name":"Venus Medtech (China)","ror":"https://ror.org/05xzt2h26","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210157345"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanbo Xue","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, Beijing, China","institution_ids":["https://openalex.org/I4210157345"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210157345"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1279","last_page":"1287"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.994700014591217,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9884999990463257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5900999903678894},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4948999881744385},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4406000077724457},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.3977999985218048},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3716999888420105},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.3082999885082245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6823999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6007000207901001},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5900999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5471000075340271},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4948999881744385},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2775000035762787},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3404835.3462936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404835.3462936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.05563","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.05563","pdf_url":"https://arxiv.org/pdf/2105.05563","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:2105.05563","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.05563","pdf_url":"https://arxiv.org/pdf/2105.05563","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":24,"referenced_works":["https://openalex.org/W2002834872","https://openalex.org/W2011720192","https://openalex.org/W2074694452","https://openalex.org/W2090883204","https://openalex.org/W2094286023","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2509235963","https://openalex.org/W2517540742","https://openalex.org/W2548570154","https://openalex.org/W2788490371","https://openalex.org/W2793768763","https://openalex.org/W2898085636","https://openalex.org/W2919115771","https://openalex.org/W2957191877","https://openalex.org/W2963323306","https://openalex.org/W2964182926","https://openalex.org/W2972941122","https://openalex.org/W3088777230","https://openalex.org/W3089196511","https://openalex.org/W4240935049","https://openalex.org/W6647494775"],"related_works":[],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,31,75,95],"is":[4,21,50,58,89],"a":[5,79],"critical":[6],"problem":[7],"in":[8,69],"web":[9],"search,":[10],"recommendation":[11],"systems":[12],"and":[13,52,77,109,128],"online":[14],"advertisement":[15],"displaying.":[16],"Learning":[17],"good":[18],"feature":[19],"interactions":[20],"essential":[22],"to":[23,27,46,71,120],"reflect":[24],"user's":[25],"preferences":[26],"items.":[28],"Many":[29],"CTR":[30,74,94],"models":[32,96],"based":[33],"on":[34,85,136],"deep":[35],"learning":[36],"have":[37],"been":[38],"proposed,":[39],"but":[40],"researchers":[41],"usually":[42],"only":[43],"pay":[44],"attention":[45],"whether":[47,54],"state-of-the-art":[48],"performance":[49],"achieved,":[51],"ignore":[53],"the":[55,65,73,106],"entire":[56],"framework":[57,83],"reasonable.":[59],"In":[60],"this":[61],"work,":[62],"we":[63,126],"use":[64],"discrete":[66],"choice":[67],"model":[68,110],"economics":[70],"redefine":[72],"problem,":[76],"propose":[78],"general":[80,101],"neural":[81],"network":[82],"built":[84],"self-attention":[86],"mechanism.":[87],"It":[88],"found":[90],"that":[91],"most":[92],"existing":[93,122],"align":[97],"with":[98,117],"our":[99,113,130],"proposed":[100,114],"framework.":[102],"We":[103],"also":[104],"examine":[105],"expressive":[107],"power":[108],"complexity":[111],"of":[112],"framework,":[115],"along":[116],"potential":[118],"extensions":[119],"some":[121,133],"models.":[123],"And":[124],"finally":[125],"demonstrate":[127],"verify":[129],"insights":[131],"through":[132],"experimental":[134],"results":[135],"public":[137],"datasets.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2021-04-26T00:00:00"}
