{"id":"https://openalex.org/W4414385119","doi":"https://doi.org/10.1145/3770854.3783916","title":"Scaling Recommender Transformers to One Billion Parameters","display_name":"Scaling Recommender Transformers to One Billion Parameters","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W4414385119","doi":"https://doi.org/10.1145/3770854.3783916"},"language":"en","primary_location":{"id":"doi:10.1145/3770854.3783916","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3783916","pdf_url":null,"source":null,"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 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3770854.3783916","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091814356","display_name":"Kirill Khrylchenko","orcid":"https://orcid.org/0009-0007-3640-8795"},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Kirill Khrylchenko","raw_affiliation_strings":["HSE University, Moscow, Russian Federation"],"raw_orcid":"https://orcid.org/0009-0007-3640-8795","affiliations":[{"raw_affiliation_string":"HSE University, Moscow, Russian Federation","institution_ids":["https://openalex.org/I118501908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022052407","display_name":"Artem Matveev","orcid":null},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]},{"id":"https://openalex.org/I58957048","display_name":"Yandex (Russia)","ror":"https://ror.org/04dbch786","country_code":"RU","type":"company","lineage":["https://openalex.org/I58957048"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Artem Matveev","raw_affiliation_strings":["Yandex, Moscow, Russian Federation and HSE University, Moscow, Russian Federation"],"raw_orcid":"https://orcid.org/0009-0004-0271-221X","affiliations":[{"raw_affiliation_string":"Yandex, Moscow, Russian Federation and HSE University, Moscow, Russian Federation","institution_ids":["https://openalex.org/I118501908","https://openalex.org/I58957048"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068390397","display_name":"Sergei Makeev","orcid":null},"institutions":[{"id":"https://openalex.org/I58957048","display_name":"Yandex (Russia)","ror":"https://ror.org/04dbch786","country_code":"RU","type":"company","lineage":["https://openalex.org/I58957048"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Sergei Makeev","raw_affiliation_strings":["Yandex, Moscow, Russian Federation"],"raw_orcid":"https://orcid.org/0009-0003-5451-6475","affiliations":[{"raw_affiliation_string":"Yandex, Moscow, Russian Federation","institution_ids":["https://openalex.org/I58957048"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031658527","display_name":"Vladimir Baikalov","orcid":"https://orcid.org/0009-0009-4864-2305"},"institutions":[{"id":"https://openalex.org/I173089394","display_name":"ITMO University","ror":"https://ror.org/04txgxn49","country_code":"RU","type":"education","lineage":["https://openalex.org/I173089394"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Vladimir Baikalov","raw_affiliation_strings":["ITMO University, Saint-Petersburg, Russian Federation"],"raw_orcid":"https://orcid.org/0009-0009-4864-2305","affiliations":[{"raw_affiliation_string":"ITMO University, Saint-Petersburg, Russian Federation","institution_ids":["https://openalex.org/I173089394"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.7965,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95179907,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2255","last_page":"2265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.1412999927997589,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.1412999927997589,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6937000155448914},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6725000143051147},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.508899986743927},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4205000102519989},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.40880000591278076},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3596000075340271},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.35530000925064087},{"id":"https://openalex.org/keywords/active-listening","display_name":"Active listening","score":0.3375999927520752}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6947000026702881},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6937000155448914},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6725000143051147},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4887999892234802},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C177291462","wikidata":"https://www.wikidata.org/wiki/Q423038","display_name":"Active listening","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25440001487731934},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.2522999942302704}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3770854.3783916","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3783916","pdf_url":null,"source":null,"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 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.15994","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15994","pdf_url":"https://arxiv.org/pdf/2507.15994","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2507.15994","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.15994","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3770854.3783916","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3783916","pdf_url":null,"source":null,"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 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"large":[1],"transformer":[2],"models":[3],"have":[4],"been":[5],"successfully":[6],"used":[7],"in":[8,62,96],"many":[9],"real-world":[10],"applications":[11],"such":[12],"as":[13,38,53],"natural":[14],"language":[15,97],"processing,":[16,21],"computer":[17],"vision,":[18],"and":[19],"speech":[20],"scaling":[22,60],"transformers":[23],"for":[24],"recommender":[25],"systems":[26],"remains":[27],"a":[28,39,54],"challenging":[29],"problem.":[30],"Recently,":[31],"the":[32,67,73,85],"Generative":[33],"Recommenders":[34],"framework":[35],"was":[36],"proposed":[37],"way":[40],"to":[41],"scale":[42],"beyond":[43],"typical":[44],"Deep":[45],"Learning":[46],"Recommendation":[47],"Models":[48],"(DLRMs).":[49],"By":[50],"reformulating":[51],"recommendation":[52],"sequential":[55],"transduction":[56],"task,":[57],"it":[58],"improves":[59],"properties":[61],"terms":[63],"of":[64,87],"compute.":[65],"Nevertheless,":[66],"largest":[68],"encoder":[69],"configuration":[70],"reported":[71],"by":[72],"HSTU":[74],"authors":[75],"is":[76],"only":[77],"176":[78],"million":[79],"parameters":[80],"---":[81],"far":[82],"smaller":[83],"than":[84],"hundreds":[86],"billions":[88],"(or":[89],"even":[90],"trillions)":[91],"that":[92],"are":[93],"now":[94],"common":[95],"models.":[98]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2025-09-22T00:00:00"}