{"id":"https://openalex.org/W4404478615","doi":"https://doi.org/10.1145/3686081.3686100","title":"Machine Learning-Based Market Segmentation and Consumer Behavior Prediction Models","display_name":"Machine Learning-Based Market Segmentation and Consumer Behavior Prediction Models","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4404478615","doi":"https://doi.org/10.1145/3686081.3686100"},"language":"en","primary_location":{"id":"doi:10.1145/3686081.3686100","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686081.3686100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science &amp; Management","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":null,"display_name":"Sheng Liu","orcid":"https://orcid.org/0009-0008-1384-4057"},"institutions":[{"id":"https://openalex.org/I141103825","display_name":"Nanchang Institute of Technology","ror":"https://ror.org/00avfj807","country_code":"CN","type":"education","lineage":["https://openalex.org/I141103825"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sheng Liu","raw_affiliation_strings":["College of Business Adminstration, Nanchang Institute of Technology, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0008-1384-4057","affiliations":[{"raw_affiliation_string":"College of Business Adminstration, Nanchang Institute of Technology, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I141103825"]}]},{"author_position":"last","author":{"id":null,"display_name":"Shixun Yang","orcid":"https://orcid.org/0009-0002-9098-0542"},"institutions":[{"id":"https://openalex.org/I141103825","display_name":"Nanchang Institute of Technology","ror":"https://ror.org/00avfj807","country_code":"CN","type":"education","lineage":["https://openalex.org/I141103825"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shixun Yang","raw_affiliation_strings":["College of International Exchange, Nanchang Institute of Technology, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0002-9098-0542","affiliations":[{"raw_affiliation_string":"College of International Exchange, Nanchang Institute of Technology, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I141103825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141103825"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29110672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"122","last_page":"126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9764000177383423,"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"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9764000177383423,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9661999940872192,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9642000198364258,"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":0.6442275047302246},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.617834210395813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5689343810081482},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5383134484291077},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5253079533576965},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.15264037251472473},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12361365556716919}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6442275047302246},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.617834210395813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5689343810081482},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5383134484291077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5253079533576965},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.15264037251472473},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12361365556716919}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3686081.3686100","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686081.3686100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science &amp; Management","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":10,"referenced_works":["https://openalex.org/W3139235965","https://openalex.org/W3165130983","https://openalex.org/W3215940191","https://openalex.org/W4210572284","https://openalex.org/W4224013881","https://openalex.org/W4229000122","https://openalex.org/W4281550840","https://openalex.org/W4297194887","https://openalex.org/W4376651145","https://openalex.org/W4386544814"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2592395359","https://openalex.org/W2535231171","https://openalex.org/W2045342254","https://openalex.org/W1501331687","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264"],"abstract_inverted_index":{"This":[0,165],"study":[1],"aims":[2],"to":[3,60,99,123,178,188],"explore":[4],"the":[5,62,125,129,135,142,151],"application":[6],"of":[7,64,128,153],"machine":[8,154,175],"learning":[9,155,176],"technology":[10,156,177],"in":[11,146,157],"market":[12,32,68,75,159,180,191,200],"segmentation":[13,33,69],"and":[14,39,56,84,96,112,117,161,182,193],"consumer":[15,41,148,162,183,194],"behavior":[16,42,88,163,184],"prediction.":[17,164],"By":[18],"utilizing":[19],"large-scale":[20],"data":[21,52,57,95],"sets":[22],"from":[23],"e-commerce":[24],"platforms,":[25],"this":[26],"paper":[27],"builds":[28],"two":[29],"models:":[30],"a":[31,40,170],"model":[34,44,65,70,138,145],"based":[35,45,108],"on":[36,46,109],"K-means":[37,143],"clustering":[38,144],"prediction":[43,89],"random":[47,136],"forest.":[48],"Data":[49],"preprocessing":[50],"includes":[51],"cleaning,":[53],"feature":[54],"engineering,":[55],"standardization,":[58],"aiming":[59],"optimize":[61],"quality":[63],"inputs.":[66],"The":[67,131],"divides":[71],"consumers":[72],"into":[73],"different":[74],"segments":[76],"by":[77],"analyzing":[78],"their":[79,101],"purchasing":[80],"behavior,":[81,149],"age,":[82],"gender":[83],"other":[85],"characteristics.":[86],"Consumer":[87],"models":[90],"use":[91],"users\u2019":[92],"historical":[93],"purchase":[94,103],"personal":[97],"characteristics":[98],"predict":[100],"future":[102],"behavior.":[104],"Model":[105],"evaluation":[106],"is":[107],"precision,":[110],"recall":[111],"F1":[113],"scores,":[114],"while":[115],"cross-validation":[116],"parameter":[118],"optimization":[119],"techniques":[120],"are":[121],"used":[122],"improve":[124],"generalization":[126],"ability":[127],"model.":[130],"results":[132],"show":[133],"that":[134],"forest":[137],"performs":[139],"better":[140,189],"than":[141],"predicting":[147],"proving":[150],"effectiveness":[152],"precise":[158],"analysis":[160,181],"research":[166],"provides":[167],"enterprises":[168,187],"with":[169],"practical":[171],"framework":[172],"for":[173],"using":[174],"conduct":[179],"prediction,":[185],"helping":[186],"understand":[190],"dynamics":[192],"needs,":[195],"thereby":[196],"formulating":[197],"more":[198],"effective":[199],"strategies.":[201]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
