{"id":"https://openalex.org/W7138408403","doi":"https://doi.org/10.1609/aaai.v40i17.38505","title":"Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation","display_name":"Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138408403","doi":"https://doi.org/10.1609/aaai.v40i17.38505"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i17.38505","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38505","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i17.38505","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129647842","display_name":"Yu Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yu Hou","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129668404","display_name":"Won-Yong Shin","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won-Yong Shin","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5129647842"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7150838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"17","first_page":"14848","last_page":"14855"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.6312999725341797,"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":0.6312999725341797,"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/T10028","display_name":"Topic Modeling","score":0.09549999982118607,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.041600000113248825,"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/lexical-analysis","display_name":"Lexical analysis","score":0.6861000061035156},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5573999881744385},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5178999900817871},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.48510000109672546},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4350999891757965},{"id":"https://openalex.org/keywords/distributional-semantics","display_name":"Distributional semantics","score":0.42309999465942383},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.36070001125335693},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3398999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8442000150680542},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.6861000061035156},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5178999900817871},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47850000858306885},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42649999260902405},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.42309999465942383},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41200000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.362199991941452},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3384000062942505},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.30820000171661377},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C98954769","wikidata":"https://www.wikidata.org/wiki/Q1759657","display_name":"Lexical semantics","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i17.38505","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38505","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i17.38505","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38505","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"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":{"Large":[0],"language":[1,20],"model":[2,163],"(LLM)-based":[3],"recommender":[4],"systems":[5],"have":[6],"achieved":[7],"high-quality":[8],"performance":[9,217],"by":[10,229],"bridging":[11],"the":[12,15,19,42,129,162,182,202],"discrepancy":[13],"between":[14],"item":[16,23,27,37,48,71,101],"space":[17,21,115],"and":[18,45,209,211],"through":[22,116],"tokenization.":[24],"However,":[25],"existing":[26,230],"tokenization":[28,57,94],"methods":[29],"typically":[30],"require":[31],"training":[32],"separate":[33],"models":[34],"for":[35,171],"each":[36,172],"domain,":[38],"limiting":[39],"generalization.":[40],"Moreover,":[41],"diverse":[43,219],"distributions":[44],"semantics":[46],"across":[47,99,144,151,218],"domains":[49,107,220],"make":[50],"it":[51],"difficult":[52],"to":[53,86,124,127,147,192],"construct":[54],"a":[55,69,82,112,117,133,155,225],"unified":[56,113],"that":[58,74,181],"preserves":[59],"domain-specific":[60,125],"information.":[61],"To":[62],"address":[63],"these":[64],"challenges,":[65],"we":[66,153],"propose":[67],"UniTok,":[68],"Unified":[70],"Tokenization":[72],"framework":[73,185],"integrates":[75],"our":[76,206],"own":[77],"mixture-of-experts":[78],"(MoE)":[79],"architecture":[80],"with":[81],"series":[83],"of":[84,168,205],"codebooks":[85],"convert":[87],"items":[88,104],"into":[89,111],"discrete":[90],"tokens,":[91],"enabling":[92],"scalable":[93],"while":[95,132],"preserving":[96],"semantic":[97,149,169],"information":[98,157,170],"multiple":[100],"domains.":[102,145],"Specifically,":[103],"from":[105],"different":[106],"are":[108,121],"first":[109],"projected":[110],"latent":[114],"shared":[118,134],"encoder.":[119],"They":[120],"then":[122],"routed":[123],"experts":[126],"capture":[128],"unique":[130],"semantics,":[131],"expert,":[135],"which":[136,160],"is":[137,186],"always":[138],"active,":[139],"encodes":[140],"common":[141],"knowledge":[142],"transferable":[143],"Additionally,":[146],"mitigate":[148],"imbalance":[150],"domains,":[152],"present":[154],"mutual":[156],"calibration":[158],"mechanism,":[159],"guides":[161],"towards":[164],"retaining":[165],"similar":[166],"levels":[167],"domain.":[173],"Comprehensive":[174],"experiments":[175],"on":[176],"wide-ranging":[177],"real-world":[178],"datasets":[179],"demonstrate":[180],"proposed":[183],"UniTok":[184],"(a)":[187],"highly":[188,213],"effective:":[189],"achieving":[190],"up":[191],"51.89%":[193],"improvements":[194],"over":[195],"strong":[196],"benchmarks,":[197],"(b)":[198],"theoretically":[199],"sound:":[200],"showing":[201],"analytical":[203],"validity":[204],"architectural":[207],"design":[208],"optimization;":[210],"(c)":[212],"generalizable:":[214],"demonstrating":[215],"robust":[216],"without":[221],"requiring":[222],"per-domain":[223],"retraining,":[224],"capability":[226],"not":[227],"supported":[228],"baselines.":[231]},"counts_by_year":[],"updated_date":"2026-03-20T20:47:17.329874","created_date":"2026-03-18T00:00:00"}