{"id":"https://openalex.org/W7138079921","doi":"https://doi.org/10.1609/aaai.v40i17.38504","title":"RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation","display_name":"RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138079921","doi":"https://doi.org/10.1609/aaai.v40i17.38504"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i17.38504","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38504","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.v40i17.38504","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129660231","display_name":"Min Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min Hou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116269221","display_name":"Chenxi Bai","orcid":"https://orcid.org/0009-0008-7944-9703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenxi Bai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129753147","display_name":"Le Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le Wu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129684590","display_name":"Hao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129643647","display_name":"Kai Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129689278","display_name":"Weiwen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weiwen Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129676354","display_name":"Richang Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richang Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129739661","display_name":"Ruiming Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruiming Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129698073","display_name":"Meng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2654321,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"17","first_page":"14838","last_page":"14847"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.20630000531673431,"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/T10028","display_name":"Topic Modeling","score":0.20630000531673431,"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":0.13379999995231628,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0820000022649765,"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/generality","display_name":"Generality","score":0.8179000020027161},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5476999878883362},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5264999866485596},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4853000044822693},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4652000069618225},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.450300008058548},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.42239999771118164},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.3946000039577484}],"concepts":[{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.8179000020027161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8162000179290771},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5476999878883362},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44999998807907104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43290001153945923},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.3946000039577484},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36419999599456787},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2778383056","wikidata":"https://www.wikidata.org/wiki/Q873636","display_name":"Namespace","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i17.38504","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38504","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"},{"id":"pmh:oai:ojs.aaai.org:article/38504","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/38504","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i17.38504","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38504","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":[{"id":"https://openalex.org/G3545516897","display_name":null,"funder_award_id":"2021ZD0111802","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5586753922","display_name":null,"funder_award_id":"U23B2031","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5689274675","display_name":null,"funder_award_id":"72188101","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8480155402","display_name":null,"funder_award_id":"72188101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326536","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64"},{"id":"https://openalex.org/F4320328099","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4,93],"achieved":[5],"remarkable":[6],"success":[7],"in":[8,76,168],"recent":[9],"years,":[10],"owing":[11],"to":[12,53,131,160],"their":[13],"impressive":[14],"generalization":[15],"capabilities":[16],"and":[17,60,96,110,165,201,212,232,239],"rich":[18],"world":[19],"knowledge.":[20,136],"To":[21],"capitalize":[22],"on":[23,36,69,226],"the":[24,162,166,169,177,180,192,204,237,242],"potential":[25],"of":[26,141,179,241],"using":[27,126],"LLMs":[28,55],"as":[29,52,203],"recommender":[30],"systems,":[31],"mainstream":[32],"approaches":[33],"typically":[34],"focus":[35],"two":[37,86,182],"paradigms.":[38],"The":[39,65],"first":[40],"paradigm":[41,67],"designs":[42],"multi-domain":[43],"or":[44,188],"multi-task":[45],"instruction":[46,129],"data":[47,130],"for":[48],"generalizable":[49,109],"recommendation,":[50],"so":[51],"align":[54,132],"with":[56,62,119,134,199,220],"general":[57],"recommendation":[58,72,78,113,128,135,234],"areas":[59],"deal":[61],"cold-start":[63,233],"recommendation.":[64],"second":[66],"focuses":[68],"enhancing":[70],"domain-specific":[71,148,214,222],"tasks,":[73],"improving":[74],"performance":[75],"warm":[77,231],"scenarios.":[79],"While":[80],"most":[81],"previous":[82],"works":[83],"treat":[84],"these":[85],"paradigms":[87,183],"separately,":[88],"we":[89,106,145],"argue":[90],"that":[91],"they":[92],"complementary":[94],"advantages,":[95],"combining":[97],"them":[98],"can":[99,216],"yield":[100],"better":[101],"results.":[102],"In":[103],"this":[104],"paper,":[105],"propose":[107],"a":[108,121,142,147],"efficient":[111,198],"LLM-based":[112],"framework":[114],"RecCocktail.":[115,244],"Our":[116],"approach":[117],"begins":[118],"fine-tuning":[120],"\"base":[122,163,205],"spirit\"":[123,164,206],"LoRA":[124,150,207],"module":[125],"domain-general":[127],"LLM":[133],"Next,":[137],"given":[138],"users'":[139],"behavior":[140],"specific":[143],"domain,":[144],"construct":[146],"\"ingredient\"":[149,167,215],"module.":[151],"We":[152],"then":[153],"provide":[154],"an":[155],"entropy-guided":[156],"adaptive":[157],"merging":[158],"method":[159],"mix":[161],"weight":[170],"space.":[171],"Please":[172],"note":[173],"that,":[174],"RecCocktail":[175,196],"combines":[176],"advantages":[178],"existing":[181],"without":[184],"introducing":[185],"additional":[186],"time":[187],"space":[189],"overhead":[190],"during":[191],"inference":[193],"phase.":[194],"Moreover,":[195],"is":[197,208],"plug":[200],"play,":[202],"trained":[209],"only":[210,221],"once,":[211],"any":[213],"be":[217],"efficiently":[218],"mixed":[219],"fine-tuning.":[223],"Extensive":[224],"experiments":[225],"multiple":[227],"datasets":[228],"under":[229],"both":[230],"scenarios":[235],"validate":[236],"effectiveness":[238],"generality":[240],"proposed":[243]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}