{"id":"https://openalex.org/W4220731207","doi":"https://doi.org/10.1109/tnnls.2022.3155690","title":"Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks","display_name":"Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks","publication_year":2022,"publication_date":"2022-03-23","ids":{"openalex":"https://openalex.org/W4220731207","doi":"https://doi.org/10.1109/tnnls.2022.3155690","pmid":"https://pubmed.ncbi.nlm.nih.gov/35320107"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3155690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3155690","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chaojie Li","orcid":"https://orcid.org/0000-0002-0557-1481"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Chaojie Li","raw_affiliation_strings":["Department of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-0557-1481","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wensen Jiang","orcid":"https://orcid.org/0000-0003-4284-8299"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wensen Jiang","raw_affiliation_strings":["Alibaba Group, Zhejiang, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4284-8299","affiliations":[{"raw_affiliation_string":"Alibaba Group, Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yin Yang","orcid":"https://orcid.org/0000-0002-0549-3882"},"institutions":[{"id":"https://openalex.org/I4210144839","display_name":"Hamad bin Khalifa University","ror":"https://ror.org/03eyq4y97","country_code":"QA","type":"education","lineage":["https://openalex.org/I4210144839"]}],"countries":["QA"],"is_corresponding":false,"raw_author_name":"Yin Yang","raw_affiliation_strings":["College of Science and Engineering, Hamad Bin Khalifa University, Education City, Doha, Qatar"],"raw_orcid":"https://orcid.org/0000-0002-0549-3882","affiliations":[{"raw_affiliation_string":"College of Science and Engineering, Hamad Bin Khalifa University, Education City, Doha, Qatar","institution_ids":["https://openalex.org/I4210144839"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shirui Pan","orcid":"https://orcid.org/0000-0003-0794-527X"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shirui Pan","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Monash University, Clayton, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0794-527X","affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gang Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Huang","raw_affiliation_strings":["Alibaba Group, Zhejiang, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group, Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lijie Guo","orcid":"https://orcid.org/0000-0002-3373-8111"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijie Guo","raw_affiliation_strings":["Alibaba Group, Zhejiang, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3373-8111","affiliations":[{"raw_affiliation_string":"Alibaba Group, Zhejiang, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I31746571"],"apc_list":null,"apc_paid":null,"fwci":1.351,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.81565106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"34","issue":"11","first_page":"9102","last_page":"9115"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.569599986076355,"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":0.569599986076355,"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/T12384","display_name":"Customer churn and segmentation","score":0.14830000698566437,"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"}},{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.03819999843835831,"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/sales-forecasting","display_name":"Sales forecasting","score":0.6218000054359436},{"id":"https://openalex.org/keywords/sales-promotion","display_name":"Sales promotion","score":0.6083999872207642},{"id":"https://openalex.org/keywords/sales-management","display_name":"Sales management","score":0.5763000249862671},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.5357999801635742},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5288000106811523},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4465999901294708},{"id":"https://openalex.org/keywords/promotion","display_name":"Promotion (chess)","score":0.41839998960494995},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41670000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711000204086304},{"id":"https://openalex.org/C2984642479","wikidata":"https://www.wikidata.org/wiki/Q7404320","display_name":"Sales forecasting","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C2781402841","wikidata":"https://www.wikidata.org/wiki/Q1259790","display_name":"Sales promotion","level":3,"score":0.6083999872207642},{"id":"https://openalex.org/C139749660","wikidata":"https://www.wikidata.org/wiki/Q5657855","display_name":"Sales management","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5357999801635742},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5288000106811523},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C98147612","wikidata":"https://www.wikidata.org/wiki/Q215599","display_name":"Promotion (chess)","level":3,"score":0.41839998960494995},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.34779998660087585},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C2991860356","wikidata":"https://www.wikidata.org/wiki/Q126793","display_name":"Retail sales","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2818000018596649},{"id":"https://openalex.org/C2781161759","wikidata":"https://www.wikidata.org/wiki/Q24451192","display_name":"Lost sales","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.257099986076355},{"id":"https://openalex.org/C23906176","wikidata":"https://www.wikidata.org/wiki/Q727515","display_name":"Affinity analysis","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2022.3155690","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3155690","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35320107","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35320107","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/420789","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/420789","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5289395183","display_name":null,"funder_award_id":"DE210100274","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G602987996","display_name":null,"funder_award_id":"DP200101197","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"},{"id":"https://openalex.org/F4320337212","display_name":"Digital Grid Futures Institute, University of New South Wales Canberra","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1997754540","https://openalex.org/W2064675550","https://openalex.org/W2067505258","https://openalex.org/W2121452542","https://openalex.org/W2157331557","https://openalex.org/W2224902226","https://openalex.org/W2336991691","https://openalex.org/W2604314403","https://openalex.org/W2607045400","https://openalex.org/W2613328025","https://openalex.org/W2745413481","https://openalex.org/W2802314367","https://openalex.org/W2809418595","https://openalex.org/W2889512547","https://openalex.org/W2895979207","https://openalex.org/W2901657898","https://openalex.org/W2904832339","https://openalex.org/W2907492528","https://openalex.org/W2907586903","https://openalex.org/W2963066159","https://openalex.org/W2963076818","https://openalex.org/W2963323306","https://openalex.org/W2964051675","https://openalex.org/W2972862002","https://openalex.org/W2984864928","https://openalex.org/W2988044524","https://openalex.org/W2996565520","https://openalex.org/W2997024057","https://openalex.org/W3080253043","https://openalex.org/W3080418372","https://openalex.org/W3096441176","https://openalex.org/W6636918297","https://openalex.org/W6679434410","https://openalex.org/W6730160768","https://openalex.org/W6738964360","https://openalex.org/W6740952796","https://openalex.org/W6742058293","https://openalex.org/W6745609711","https://openalex.org/W6754005058","https://openalex.org/W6755207826","https://openalex.org/W6764598968","https://openalex.org/W6767140673","https://openalex.org/W6785647295","https://openalex.org/W6785876357"],"related_works":[],"abstract_inverted_index":{"Many":[0],"e-commerce":[1],"platforms,":[2],"such":[3,12],"as":[4],"AliExpress,":[5],"run":[6],"major":[7,284],"promotion":[8,146],"campaigns":[9],"regularly.":[10],"Before":[11],"a":[13,43,66,158,230,241,283,299],"promotion,":[14,285],"it":[15],"is":[16,70,168,223,238],"important":[17],"to":[18,80,83,104,109,139,169,225,247,273,312],"predict":[19,170,248],"potential":[20,279],"best":[21],"sellers":[22],"and":[23,37,263,298],"their":[24,34],"respective":[25],"sales":[26,46,49,63,88,116,150,172,187,251,276,316],"volumes":[27,252],"so":[28],"that":[29,96,269,303],"the":[30,55,62,84,91,110,119,126,165,171,195,210,249,260,264,267,304],"platform":[31],"can":[32,51],"arrange":[33],"supply":[35],"chains":[36],"logistics":[38],"accordingly.":[39],"For":[40],"items":[41,281],"with":[42,73,181,184],"sufficiently":[44,185],"long":[45,186],"history,":[47],"accurate":[48,106,275],"forecast":[50],"be":[52],"achieved":[53],"through":[54,178],"traditional":[56],"statistical":[57],"forecasting":[58],"techniques.":[59],"Accurately":[60],"predicting":[61],"volume":[64,117,173],"of":[65,90,112,115,164,174,253,258],"new":[67,92,176,221,254],"item,":[68,93],"however,":[69],"rather":[71],"challenging":[72],"existing":[74,313],"methods;":[75],"time":[76],"series":[77],"models":[78,95],"tend":[79],"overfit":[81],"due":[82,108],"very":[85],"limited":[86],"historical":[87,100],"records":[89],"whereas":[94],"do":[97],"not":[98],"utilize":[99],"information":[101],"often":[102],"fail":[103],"make":[105],"predictions,":[107],"lack":[111],"strong":[113],"indicators":[114],"among":[118],"item's":[120],"basic":[121],"attributes.":[122],"This":[123],"article":[124],"presents":[125],"solution":[127,167,193,212,306],"deployed":[128],"at":[129],"Alibaba":[130],"in":[131,137,157,206,218,282],"2019,":[132],"which":[133,201,219,286],"had":[134],"been":[135,203],"used":[136],"production":[138],"prepare":[140],"for":[141,278,315],"its":[142,179],"annual":[143],"\"Double":[144],"11\"":[145],"event":[147],"whose":[148],"total":[149],"amount":[151],"exceeded":[152],"U.S.":[153],"$":[154],"38":[155],"billion":[156],"single":[159],"day.":[160],"The":[161,256],"main":[162],"idea":[163],"proposed":[166,211,305],"each":[175,220],"item":[177,216,222,261],"connections":[180],"older":[182,227],"products":[183],"history.":[188],"In":[189],"other":[190],"words,":[191],"our":[192],"considers":[194],"cross-selling":[196],"effects":[197],"between":[198],"different":[199],"products,":[200],"has":[202],"largely":[204],"neglected":[205],"previous":[207],"methods.":[208],"Specifically,":[209],"first":[213],"constructs":[214],"an":[215],"graph,":[217],"connected":[224],"relevant":[226],"items.":[228,255],"Then,":[229],"novel":[231],"multitask":[232],"graph":[233,262],"convolutional":[234],"neural":[235],"network":[236],"(GCN)":[237],"trained":[239],"by":[240],"multiobjective":[242],"optimization-based":[243],"gradient":[244],"surgery":[245],"technique":[246],"expected":[250],"designs":[257],"both":[259,294],"GCN":[265],"exploit":[266],"fact":[268],"we":[270],"only":[271],"need":[272],"perform":[274],"forecasts":[277],"best-selling":[280],"helps":[287],"reduce":[288],"computational":[289],"overhead.":[290],"Extensive":[291],"experiments":[292],"on":[293],"proprietary":[295],"AliExpress":[296],"data":[297],"public":[300],"dataset":[301],"demonstrate":[302],"achieves":[307],"consistent":[308],"performance":[309],"gains":[310],"compared":[311],"methods":[314],"forecast.":[317]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-10T08:33:47.465468","created_date":"2022-04-03T00:00:00"}
