{"id":"https://openalex.org/W3031101592","doi":"https://doi.org/10.1145/3383972.3384046","title":"A Feature Interaction Network for Customer Churn Prediction","display_name":"A Feature Interaction Network for Customer Churn Prediction","publication_year":2020,"publication_date":"2020-02-15","ids":{"openalex":"https://openalex.org/W3031101592","doi":"https://doi.org/10.1145/3383972.3384046","mag":"3031101592"},"language":"en","primary_location":{"id":"doi:10.1145/3383972.3384046","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3384046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","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/A5101727117","display_name":"Qi Tang","orcid":"https://orcid.org/0009-0002-5975-4635"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qi Tang","raw_affiliation_strings":["College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053171458","display_name":"Guoen Xia","orcid":"https://orcid.org/0000-0002-8191-4490"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]},{"id":"https://openalex.org/I163732180","display_name":"Guangxi University of Finance and Economics","ror":"https://ror.org/02ayg6516","country_code":"CN","type":"education","lineage":["https://openalex.org/I163732180"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoen Xia","raw_affiliation_strings":["College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China and School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China and School of Business Administration, Guangxi University of Finance and Economics, Nanning, Guangxi, China","institution_ids":["https://openalex.org/I163732180","https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085013836","display_name":"Xianquan Zhang","orcid":"https://orcid.org/0000-0003-3359-117X"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianquan Zhang","raw_affiliation_strings":["College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061021493","display_name":"Yaxiang Li","orcid":"https://orcid.org/0000-0002-6631-5617"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaxiang Li","raw_affiliation_strings":["College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Information Engineering, Guangxi Normal University, Guiling, Guangxi, China","institution_ids":["https://openalex.org/I29739308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101727117"],"corresponding_institution_ids":["https://openalex.org/I29739308"],"apc_list":null,"apc_paid":null,"fwci":0.5456,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74775618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"242","last_page":"248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9998999834060669,"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":0.9998999834060669,"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9916999936103821,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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":0.7815607786178589},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7232563495635986},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6464508771896362},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5429394841194153},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.48875880241394043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4829815924167633},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4702148735523224},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4687143862247467},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3517865538597107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.24625056982040405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7815607786178589},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7232563495635986},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6464508771896362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5429394841194153},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.48875880241394043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4829815924167633},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4702148735523224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4687143862247467},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3517865538597107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.24625056982040405},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"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},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3383972.3384046","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3383972.3384046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 12th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W64669240","https://openalex.org/W292604860","https://openalex.org/W2001706356","https://openalex.org/W2010541215","https://openalex.org/W2079286497","https://openalex.org/W2101802482","https://openalex.org/W2139212933","https://openalex.org/W2162772535","https://openalex.org/W2185726469","https://openalex.org/W2187119972","https://openalex.org/W2295598076","https://openalex.org/W2295739661","https://openalex.org/W2460450520","https://openalex.org/W2464785945","https://openalex.org/W2473313289","https://openalex.org/W2483412448","https://openalex.org/W2560738241","https://openalex.org/W2615973898","https://openalex.org/W2779766484","https://openalex.org/W2792328488","https://openalex.org/W2793003883","https://openalex.org/W2793768763","https://openalex.org/W2977750641","https://openalex.org/W2999549716","https://openalex.org/W3102476541","https://openalex.org/W3104030692","https://openalex.org/W4242442510","https://openalex.org/W4360789479"],"related_works":["https://openalex.org/W3008173435","https://openalex.org/W2750075801","https://openalex.org/W3164948662","https://openalex.org/W4400413234","https://openalex.org/W4388651014","https://openalex.org/W3153597579","https://openalex.org/W4385336128","https://openalex.org/W4394398790","https://openalex.org/W4399455186","https://openalex.org/W2963507914"],"abstract_inverted_index":{"Customer":[0],"churn":[1,25,44],"prediction":[2,45],"is":[3,28,116],"an":[4,99],"active":[5],"research":[6],"topic":[7],"for":[8],"the":[9,38,83,113],"data":[10],"mining":[11],"community":[12],"and":[13,63,89,103],"business":[14],"managers":[15],"in":[16,52],"this":[17,70],"rapidly":[18],"growing":[19],"society.":[20],"The":[21],"ability":[22],"to":[23,35,81],"detect":[24],"customers":[26],"precisely":[27],"something":[29],"that":[30,118],"every":[31],"company":[32],"would":[33],"wish":[34],"achieve.":[36],"With":[37],"great":[39],"success":[40],"of":[41,86],"DNNs,":[42],"several":[43,109,128],"models":[46,130],"based":[47],"on":[48,131],"DNNs":[49,57],"are":[50],"proposed":[51,73,120],"recent":[53],"years.":[54],"However,":[55],"traditional":[56],"cannot":[58],"learn":[59,90],"high-order":[60,91],"feature":[61,75,92],"interactions":[62],"deal":[64],"with":[65,108],"one-hot":[66],"vectors":[67],"well.":[68],"In":[69],"paper,":[71],"we":[72],"a":[74,104,123],"interaction":[76],"network":[77,95,102,107],"(FIN),":[78],"which":[79],"aims":[80],"enhance":[82],"inherent":[84],"relations":[85],"discrete":[87],"features":[88],"interactions.":[93],"This":[94],"contains":[96],"two":[97],"modules:":[98],"entity":[100],"embedding":[101],"factorization":[105],"machine":[106],"sliding":[110],"windows.":[111],"From":[112],"experiments,":[114],"it":[115],"observed":[117],"our":[119],"model":[121],"has":[122],"better":[124],"predictive":[125],"performance":[126],"than":[127],"state-of-the-art":[129],"4":[132],"public":[133],"datasets.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
