{"id":"https://openalex.org/W7150734059","doi":"https://doi.org/10.48550/arxiv.2604.02684","title":"MBGR: Multi-Business Prediction for Generative Recommendation at Meituan","display_name":"MBGR: Multi-Business Prediction for Generative Recommendation at Meituan","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150734059","doi":"https://doi.org/10.48550/arxiv.2604.02684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02684","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.02684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133048365","display_name":"Changhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Changhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101977822","display_name":"Junwei Yin","orcid":"https://orcid.org/0009-0000-2617-0436"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Junwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102378608","display_name":"Zhilin Zeng","orcid":"https://orcid.org/0000-0002-5084-1145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Zhilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003616850","display_name":"S. C. Kou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kou, Senjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133030526","display_name":"Shuli Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034573842","display_name":"W. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wenshuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018664938","display_name":"Yinhua Zhu","orcid":"https://orcid.org/0009-0002-9135-7488"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yinhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133004823","display_name":"Haitao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133009026","display_name":"Xingxing Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xingxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.7771999835968018,"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":0.7771999835968018,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.021400000900030136,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.012900000438094139,"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/generative-grammar","display_name":"Generative grammar","score":0.5831000208854675},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.541700005531311},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5184999704360962},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4293000102043152},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4172999858856201},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.35109999775886536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8044000267982483},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5831000208854675},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.541700005531311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5393000245094299},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4490000009536743},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4293000102043152},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4172999858856201},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C2986420190","wikidata":"https://www.wikidata.org/wiki/Q39045939","display_name":"Semantic space","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2921999990940094},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C6881194","wikidata":"https://www.wikidata.org/wiki/Q7449091","display_name":"Semantic technology","level":4,"score":0.2768999934196472},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.26899999380111694}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02684","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02684","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0,87],"recommendation":[1],"(GR)":[2],"has":[3],"recently":[4],"emerged":[5],"as":[6],"a":[7,45,64,107,123,136],"promising":[8],"paradigm":[9],"for":[10,95],"industrial":[11],"recommendations.":[12],"GR":[13,37,92],"leverages":[14],"Semantic":[15],"IDs":[16],"(SIDs)":[17],"to":[18,31,53,56,73,128,150],"reduce":[19],"the":[20,25,90,153],"encoding-decoding":[21],"space":[22,67],"and":[23,62,159,170],"employs":[24],"Next":[26],"Token":[27],"Prediction":[28,125],"(NTP)":[29],"framework":[30,93,99],"explore":[32],"scaling":[33],"laws.":[34],"However,":[35],"existing":[36],"methods":[38],"suffer":[39],"from":[40],"two":[41],"critical":[42],"issues:":[43],"(1)":[44],"\\textbf{seesaw":[46],"phenomenon}":[47],"in":[48,176],"multi-business":[49,96,145,154],"scenarios":[50],"arises":[51],"due":[52],"NTP's":[54],"inability":[55],"capture":[57],"complex":[58],"cross-business":[59],"behavioral":[60],"patterns;":[61],"(2)":[63],"unified":[65],"SID":[66],"causes":[68],"\\textbf{representation":[69],"confusion}":[70],"by":[71],"failing":[72],"distinguish":[74],"distinct":[75],"semantic":[76,109,115],"information":[77],"across":[78],"businesses.":[79],"To":[80],"address":[81],"these":[82],"issues,":[83],"we":[84,105,121,134,171],"propose":[85],"Multi-Business":[86,124],"Recommendation":[88],"(MBGR),":[89],"first":[91],"tailored":[94],"scenarios.":[97],"Our":[98],"comprises":[100],"three":[101],"key":[102],"components.":[103],"First,":[104],"design":[106],"Business-aware":[108],"ID":[110],"(BID)":[111],"module":[112,141],"that":[113,142],"preserves":[114],"integrity":[116],"via":[117],"domain-aware":[118],"tokenization.":[119],"Then,":[120],"introduce":[122],"(MBP)":[126],"structure":[127],"provide":[129],"business-specific":[130],"prediction":[131],"capabilities.":[132],"Furthermore,":[133],"develop":[135],"Label":[137],"Dynamic":[138],"Routing":[139],"(LDR)":[140],"transforms":[143],"sparse":[144],"labels":[146,149],"into":[147],"dense":[148],"further":[151],"enhance":[152],"generation":[155],"capability.":[156],"Extensive":[157],"offline":[158],"online":[160],"experiments":[161],"on":[162],"Meituan's":[163],"food":[164],"delivery":[165],"platform":[166],"validate":[167],"MBGR's":[168],"effectiveness,":[169],"have":[172],"successfully":[173],"deployed":[174],"it":[175],"production.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}