{"id":"https://openalex.org/W7138295081","doi":"https://doi.org/10.1609/aaai.v40i18.38577","title":"LMGL-WD: LLM-Guided Multi-Task Graph Learning for Category-Level Warehouse Demand Prediction in E-Commerce","display_name":"LMGL-WD: LLM-Guided Multi-Task Graph Learning for Category-Level Warehouse Demand Prediction in E-Commerce","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138295081","doi":"https://doi.org/10.1609/aaai.v40i18.38577"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i18.38577","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38577","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38577/42539","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":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38577/42539","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129651468","display_name":"Wenjun Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210096112","display_name":"Rutgers Sexual and Reproductive Health and Rights","ror":"https://ror.org/00rcvgx40","country_code":"NL","type":"other","lineage":["https://openalex.org/I4210096112"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wenjun Lyu","raw_affiliation_strings":["JD Logistics\nRutgers University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Logistics\nRutgers University","institution_ids":["https://openalex.org/I4210096112"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129658603","display_name":"Fangyu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangyu Li","raw_affiliation_strings":["JD Logistics\nHuazhong University of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Logistics\nHuazhong University of Science and Technology","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129687428","display_name":"Yudong Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yudong Zhang","raw_affiliation_strings":["University of Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129715891","display_name":"Shuai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuai Wang","raw_affiliation_strings":["Southeast University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082653046","display_name":"Yunhuai Liu","orcid":"https://orcid.org/0000-0002-1180-8078"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhuai Liu","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129711395","display_name":"Tian He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian He","raw_affiliation_strings":["JD Logistics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Logistics","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091569655","display_name":"Desheng Zhang","orcid":"https://orcid.org/0000-0003-3005-9305"},"institutions":[{"id":"https://openalex.org/I4210096112","display_name":"Rutgers Sexual and Reproductive Health and Rights","ror":"https://ror.org/00rcvgx40","country_code":"NL","type":"other","lineage":["https://openalex.org/I4210096112"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Desheng Zhang","raw_affiliation_strings":["Rutgers University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University","institution_ids":["https://openalex.org/I4210096112"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"18","first_page":"15492","last_page":"15500"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.3467999994754791,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.3467999994754791,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.07280000299215317,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.05570000037550926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5055000185966492},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5037999749183655},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.43529999256134033},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.421999990940094},{"id":"https://openalex.org/keywords/data-warehouse","display_name":"Data warehouse","score":0.4041000008583069},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.35580000281333923},{"id":"https://openalex.org/keywords/warehouse","display_name":"Warehouse","score":0.35429999232292175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372000217437744},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4449000060558319},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.43529999256134033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43050000071525574},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3684000074863434},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C50416739","wikidata":"https://www.wikidata.org/wiki/Q181623","display_name":"Warehouse","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.2646999955177307}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i18.38577","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38577","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38577/42539","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/38577","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/38577","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/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i18.38577","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38577","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38577/42539","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327777","display_name":"Jiangsu Provincial Key Research and Development Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138295081.pdf","grobid_xml":"https://content.openalex.org/works/W7138295081.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,36],"warehouse-based":[1],"e-commerce,":[2],"accurate":[3],"category-level":[4,98],"warehouse":[5,99],"demand":[6,55,100,156],"prediction":[7],"is":[8,59],"essential":[9],"to":[10,23,29,44,53,114,129,138,149,154,185,188],"ensure":[11],"effective":[12],"inventory":[13],"management.":[14],"Existing":[15],"works":[16],"mainly":[17],"explore":[18,40],"advanced":[19],"time":[20],"series":[21,111,121],"models":[22,43],"capture":[24,151],"the":[25,46,65,70,78,85,132,168],"temporal":[26],"dynamics,":[27],"failing":[28],"mine":[30,131],"cross-category":[31,145,152],"and":[32,49,72,76,120,142],"cross-warehouse":[33,80,125],"correlations":[34,153],"effectively.":[35],"this":[37],"paper,":[38],"we":[39,88],"large":[41],"language":[42],"understand":[45],"semantic":[47],"information":[48],"fuse":[50],"multi-view":[51],"knowledge":[52,81,134],"enhance":[54,139],"prediction.":[56,101,157],"However,":[57],"it":[58],"not":[60],"trivial":[61],"due":[62],"to:":[63],"i)":[64,107],"inaccurate":[66],"LLM\u2019s":[67],"understanding":[68],"of":[69,167],"category-related":[71],"warehouse-related":[73],"textual":[74],"input;":[75],"ii)":[77,123],"complicated":[79],"utilization.":[82],"To":[83],"solve":[84],"above":[86],"challenges,":[87],"propose":[89],"an":[90,108],"LLM-guided":[91,109],"multi-task":[92,146],"graph":[93],"learning":[94,127,147],"framework,":[95],"LMGL-WD,":[96],"for":[97],"Specifically,":[102],"LMGL-WD":[103,176],"includes":[104],"three":[105],"components:":[106],"category":[110,117,126,140],"encoding":[112],"module":[113,128,148],"represent":[115],"each":[116],"through":[118],"contextual":[119],"embedding;":[122],"a":[124,144],"adaptively":[130,150],"informative":[133],"from":[135,165],"cross":[136],"warehouses":[137],"representation;":[141],"iii)":[143],"improve":[155],"Extensive":[158],"evaluation":[159],"results":[160],"with":[161],"real-world":[162],"data":[163],"collected":[164],"one":[166],"largest":[169],"e-commerce":[170],"platforms":[171],"in":[172],"China":[173],"demonstrate":[174],"that":[175],"achieves":[177],"superior":[178],"performance,":[179],"e.g.,":[180],"reduces":[181],"MAPE":[182],"by":[183],"up":[184],"31.59%,":[186],"compared":[187],"state-of-the-art":[189],"methods.":[190]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}