{"id":"https://openalex.org/W4399253536","doi":"https://doi.org/10.1145/3656766.3656775","title":"Modeling and Analysis of Telecom User churn Warning","display_name":"Modeling and Analysis of Telecom User churn Warning","publication_year":2023,"publication_date":"2023-11-24","ids":{"openalex":"https://openalex.org/W4399253536","doi":"https://doi.org/10.1145/3656766.3656775"},"language":"en","primary_location":{"id":"doi:10.1145/3656766.3656775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3656766.3656775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk 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":"https://openalex.org/A5067942935","display_name":"Yaqi Hu","orcid":"https://orcid.org/0009-0006-3470-7559"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaqi Hu","raw_affiliation_strings":["Lanzhou Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0006-3470-7559","affiliations":[{"raw_affiliation_string":"Lanzhou Jiaotong University, China","institution_ids":["https://openalex.org/I3133134087"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103168773","display_name":"Shuping Lu","orcid":"https://orcid.org/0009-0009-9446-2858"},"institutions":[{"id":"https://openalex.org/I3133134087","display_name":"Lanzhou Jiaotong University","ror":"https://ror.org/03144pv92","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133134087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijie Lu","raw_affiliation_strings":["Lanzhou Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0009-9446-2858","affiliations":[{"raw_affiliation_string":"Lanzhou Jiaotong University, China","institution_ids":["https://openalex.org/I3133134087"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31534433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"45","last_page":"50"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9958999752998352,"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":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7265655994415283},{"id":"https://openalex.org/keywords/customer-base","display_name":"Customer base","score":0.6812924742698669},{"id":"https://openalex.org/keywords/customer-retention","display_name":"Customer retention","score":0.4390282928943634},{"id":"https://openalex.org/keywords/voice-of-the-customer","display_name":"Voice of the customer","score":0.4310847818851471},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4291676878929138},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.429140567779541},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.42239969968795776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40226393938064575},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3915793001651764},{"id":"https://openalex.org/keywords/service-quality","display_name":"Service quality","score":0.1426129937171936},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.1205650269985199},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.11608090996742249},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.09234976768493652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7265655994415283},{"id":"https://openalex.org/C2777276756","wikidata":"https://www.wikidata.org/wiki/Q5196446","display_name":"Customer base","level":2,"score":0.6812924742698669},{"id":"https://openalex.org/C101276457","wikidata":"https://www.wikidata.org/wiki/Q5196474","display_name":"Customer retention","level":4,"score":0.4390282928943634},{"id":"https://openalex.org/C43595421","wikidata":"https://www.wikidata.org/wiki/Q7939596","display_name":"Voice of the customer","level":5,"score":0.4310847818851471},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4291676878929138},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.429140567779541},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.42239969968795776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40226393938064575},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3915793001651764},{"id":"https://openalex.org/C140781008","wikidata":"https://www.wikidata.org/wiki/Q1221081","display_name":"Service quality","level":3,"score":0.1426129937171936},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.1205650269985199},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.11608090996742249},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.09234976768493652},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3656766.3656775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3656766.3656775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4286775473","https://openalex.org/W1968335782","https://openalex.org/W2028712555","https://openalex.org/W2183205277","https://openalex.org/W1970053881","https://openalex.org/W2500523674","https://openalex.org/W1983787324","https://openalex.org/W2373698945","https://openalex.org/W2098919509","https://openalex.org/W2919924281"],"abstract_inverted_index":{"With":[0],"the":[1,5,36,39,90,96,147,150,156,163],"rapid":[2],"development":[3,37],"of":[4,23,53,174],"telecommunications":[6],"industry,":[7],"market":[8],"competition":[9],"has":[10,16,47,169],"become":[11,17,49],"saturated":[12],"and":[13,66,75,94,102,155],"product":[14],"homogeneity":[15],"a":[18,80,110],"serious":[19],"problem.":[20],"The":[21,127],"cost":[22],"acquiring":[24],"new":[25],"customers":[26],"is":[27,100,120],"high,":[28],"leading":[29],"operators":[30],"to":[31,35,70,108,123,137],"pay":[32],"more":[33,166],"attention":[34],"in":[38,149,172],"existing":[40],"customer":[41,44,72,85,103,116,124],"base.":[42],"Currently,":[43],"churn":[45,73,86,104,125],"prediction":[46,92,113,142,157],"gradually":[48],"an":[50],"important":[51],"aspect":[52],"business":[54,67],"analysis":[55,74],"for":[56,84,115],"operators.":[57],"Operators":[58],"typically":[59],"use":[60],"two":[61],"methods,":[62],"namely":[63],"model":[64,114,119,138],"algorithms":[65,136],"data":[68,98],"analysis,":[69],"conduct":[71],"prediction.":[76,87,126],"This":[77,118],"paper":[78],"proposes":[79],"machine":[81,111,134],"learning-based":[82],"method":[83],"Based":[88],"on":[89],"actual":[91],"requirements":[93],"standards,":[95],"initial":[97],"information":[99],"preprocessed,":[101],"features":[105],"are":[106,165],"extracted":[107],"construct":[109],"learning":[112,135],"churn.":[117],"then":[121],"applied":[122],"test":[128],"results":[129],"show":[130],"that":[131,146],"using":[132],"ensemble":[133],"yields":[139],"relatively":[140],"high":[141],"recall":[143],"rates,":[144],"indicating":[145],"errors":[148],"predictions":[151,164],"can":[152,159],"be":[153,160],"controlled":[154],"scope":[158],"expanded.":[161],"Overall,":[162],"targeted,":[167],"which":[168],"practical":[170],"significance":[171],"terms":[173],"application":[175],"innovation.":[176]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}