{"id":"https://openalex.org/W2507551188","doi":"https://doi.org/10.1145/2939672.2945388","title":"Business Applications of Predictive Modeling at Scale","display_name":"Business Applications of Predictive Modeling at Scale","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2507551188","doi":"https://doi.org/10.1145/2939672.2945388","mag":"2507551188"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2945388","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2945388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","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/A5023593453","display_name":"Qiang Zhu","orcid":"https://orcid.org/0000-0002-4677-1244"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qiang Zhu","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055813479","display_name":"Songtao Guo","orcid":"https://orcid.org/0000-0001-6741-4871"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Songtao Guo","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047814069","display_name":"Paul Ogilvie","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Ogilvie","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351193","display_name":"Yan Liu","orcid":"https://orcid.org/0009-0006-2647-2059"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Liu","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023593453"],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.10131568,"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":"2139","last_page":"2140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9819999933242798,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7265599370002747},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7196692228317261},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.6868807673454285},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.646385669708252},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.6351346969604492},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5541210770606995},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5493523478507996},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5150091648101807},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4772070348262787},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.47705450654029846},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44234806299209595},{"id":"https://openalex.org/keywords/business-model","display_name":"Business model","score":0.44066646695137024},{"id":"https://openalex.org/keywords/best-practice","display_name":"Best practice","score":0.43787655234336853},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2592637538909912},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.24550414085388184},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.2052420675754547}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7265599370002747},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7196692228317261},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.6868807673454285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.646385669708252},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.6351346969604492},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5541210770606995},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5493523478507996},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5150091648101807},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4772070348262787},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.47705450654029846},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44234806299209595},{"id":"https://openalex.org/C4216890","wikidata":"https://www.wikidata.org/wiki/Q815823","display_name":"Business model","level":2,"score":0.44066646695137024},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.43787655234336853},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2592637538909912},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24550414085388184},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2052420675754547},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2945388","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2945388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W1978196964","https://openalex.org/W1981379119"],"related_works":["https://openalex.org/W2809858895","https://openalex.org/W4321234707","https://openalex.org/W3199841521","https://openalex.org/W4285505876","https://openalex.org/W3189884647","https://openalex.org/W3123613287","https://openalex.org/W2582287177","https://openalex.org/W4401228152","https://openalex.org/W4385950391","https://openalex.org/W4311802502"],"abstract_inverted_index":{"Predictive":[0],"modeling":[1,50,82,107,145,150],"is":[2],"the":[3,45,52,95,101,111,149,168],"art":[4,102],"of":[5,14,48,67,79,100,124,142,161],"building":[6,104,143],"statistical":[7],"models":[8],"that":[9],"forecast":[10],"probabilities":[11],"and":[12,56,70,85,98,109,115,128,155,164],"trends":[13],"future":[15],"events.":[16],"It":[17],"has":[18],"broad":[19],"applications":[20,58],"in":[21,51,59,103],"industry":[22],"across":[23,64,167],"different":[24],"domains.":[25],"Some":[26],"popular":[27,162],"examples":[28,63],"include":[29],"user":[30],"intention":[31],"predictions,":[32],"lead":[33],"scoring,":[34],"churn":[35],"analysis,":[36],"etc.":[37],"In":[38],"this":[39],"tutorial,":[40],"we":[41,136],"will":[42,74,93,137],"focus":[43],"on":[44],"best":[46],"practice":[47],"predictive":[49,81,106,144],"big":[53],"data":[54,153],"era":[55],"its":[57],"industry,":[60],"with":[61,76,158],"motivating":[62],"a":[65,129],"range":[66],"business":[68,89],"tasks":[69],"relevance":[71,127],"products.":[72],"We":[73,92],"start":[75],"an":[77,159],"overview":[78,160],"how":[80],"helps":[83],"power":[84],"drive":[86],"various":[87],"key":[88,113],"use":[90],"cases.":[91],"introduce":[94],"essential":[96],"concepts":[97],"state":[99],"end-to-end":[105],"solutions,":[108],"discuss":[110,138],"challenges,":[112],"technologies,":[114],"lessons":[116],"learned":[117],"from":[118],"our":[119],"practice,":[120],"including":[121],"case":[122],"studies":[123],"LinkedIn":[125],"feed":[126],"platform":[130,146],"for":[131,152],"email":[132],"response":[133],"prediction.":[134],"Moreover,":[135],"some":[139],"practical":[140],"solutions":[141],"to":[147],"scale":[148],"efforts":[151],"scientists":[154],"analysts,":[156],"along":[157],"tools":[163],"platforms":[165],"used":[166],"industry.":[169]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
