{"id":"https://openalex.org/W3134962519","doi":"https://doi.org/10.1145/3446132.3446169","title":"Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model","display_name":"Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model","publication_year":2020,"publication_date":"2020-12-24","ids":{"openalex":"https://openalex.org/W3134962519","doi":"https://doi.org/10.1145/3446132.3446169","mag":"3134962519"},"language":"en","primary_location":{"id":"doi:10.1145/3446132.3446169","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3446132.3446169","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046462646","display_name":"Ziyang Pan","orcid":"https://orcid.org/0000-0002-2139-2029"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyang Pan","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109951954","display_name":"Zhishan Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhishan Huang","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080377333","display_name":"Xiaowen Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaowen Lin","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076921410","display_name":"Songxia Li","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songxia Li","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039180194","display_name":"Huanze Zeng","orcid":"https://orcid.org/0000-0002-6068-2854"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanze Zeng","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073166534","display_name":"Daifeng Li","orcid":"https://orcid.org/0000-0002-6727-2153"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daifeng Li","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5046462646"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.2542,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.65945581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T12384","display_name":"Customer churn and segmentation","score":0.9815999865531921,"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/T14260","display_name":"Impact of AI and Big Data on Business and Society","score":0.9732999801635742,"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/computer-science","display_name":"Computer science","score":0.7064203023910522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4980652332305908},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.48895227909088135},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4688037037849426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4090093970298767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33123844861984253},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1755223572254181}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7064203023910522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4980652332305908},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.48895227909088135},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4688037037849426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4090093970298767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33123844861984253},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1755223572254181},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3446132.3446169","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3446132.3446169","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1978375499","https://openalex.org/W2003360602","https://openalex.org/W2075665363","https://openalex.org/W2152721660","https://openalex.org/W2154266223","https://openalex.org/W2157196594","https://openalex.org/W2344333429","https://openalex.org/W2536562139","https://openalex.org/W2991289964","https://openalex.org/W3016201365","https://openalex.org/W3026890701"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,231],"intelligent":[1,121],"analysis":[2,122],"and":[3,60,93,102,136,214],"marketing":[4,47,184],"prediction":[5,185,240],"of":[6,21,33,45,63,68,78,87,237,254],"high-tech":[7],"enterprises":[8],"based":[9,125,175,186],"on":[10,27,126,187],"artificial":[11,73],"intelligence":[12],"is":[13,81,130,180],"a":[14,173,221],"hot":[15],"topic":[16],"in":[17],"the":[18,22,29,34,37,42,58,79,116,198,206,235,251,255],"field.":[19],"Most":[20],"existing":[23],"researches":[24],"mainly":[25],"focus":[26],"taking":[28],"internal":[30],"structural":[31],"features":[32],"enterprise":[35,46,69,110,238],"as":[36,153],"starting":[38],"point":[39],"to":[40,56,82,98,108,114,182,196,220],"study":[41],"influencing":[43],"factors":[44,104],"trends.":[48],"Different":[49],"with":[50,202,245],"previous":[51],"studies,":[52],"This":[53],"research":[54,80],"attempts":[55],"simulate":[57,83],"analyzing":[59,88],"decision":[61],"processes":[62,135],"domain":[64,84],"experts":[65],"towards":[66],"issues":[67],"operations":[70],"by":[71,139,224,241],"using":[72,140],"intelligent.":[74],"One":[75],"main":[76],"challenge":[77,117],"experts\u2019":[85],"behaviors":[86],"multi-data":[89,138],"including":[90],"both":[91],"structured":[92],"unstructured":[94,106],"data,":[95],"especially":[96],"how":[97],"extract":[99,211],"knowledge,":[100],"patterns":[101],"import":[103],"from":[105,228],"data":[107],"support":[109],"decisions.":[111],"In":[112],"order":[113],"solve":[115],"mentioned":[118],"above,":[119],"an":[120,141],"framework":[123],"MS-LSTM":[124,133,208],"business":[127,225],"management":[128,226],"theory":[129],"proposed.":[131],"Firstly,":[132],"collects,":[134],"analyzes":[137],"encoder":[142],"strategy":[143,150],"module,":[144],"which":[145,216,248],"contains":[146],"more":[147],"than":[148],"10":[149],"models":[151],"such":[152],"normalization,":[154],"one-hot,":[155],"distribution":[156],"fitting,":[157],"time":[158,176],"series":[159,177],"completion,":[160],"semantic":[161],"encoding,":[162],"Bert,":[163],"etc.,":[164],"providing":[165],"high":[166],"quality":[167],"input":[168],"for":[169],"downstream":[170],"tasks.":[171],"Finally,":[172],"LSTM":[174],"processing":[178],"model":[179,232],"proposed":[181,199,207],"make":[183],"upstream":[188],"processed":[189],"multi-source":[190,229],"data.":[191,230],"Extensive":[192],"experiments":[193],"are":[194],"conducted":[195],"verify":[197,250],"model.":[200],"Compared":[201],"traditional":[203],"benchmark":[204],"model,":[205],"could":[209,217],"efficiently":[210],"meaningful":[212],"knowledge":[213],"patterns,":[215],"be":[218],"explained":[219],"certain":[222],"extent":[223],"theory,":[227],"has":[233],"improved":[234],"accuracy":[236],"trend":[239],"19.3":[242],"times":[243],"compared":[244],"state-of-art":[246],"baselines,":[247],"further":[249],"application":[252],"values":[253],"research.":[256]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
