{"id":"https://openalex.org/W2138444337","doi":"https://doi.org/10.1109/tkde.2008.110","title":"Using Context to Improve Predictive Modeling of Customers in Personalization Applications","display_name":"Using Context to Improve Predictive Modeling of Customers in Personalization Applications","publication_year":2008,"publication_date":"2008-09-26","ids":{"openalex":"https://openalex.org/W2138444337","doi":"https://doi.org/10.1109/tkde.2008.110","mag":"2138444337"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2008.110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2008.110","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-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/A5078825283","display_name":"Cosimo Palmisano","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C. Palmisano","raw_affiliation_strings":["Aizoon Consulting S.r.l, Turin, Italy","Aizoon Consulting S.r.l, Turin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aizoon Consulting S.r.l, Turin, Italy","institution_ids":[]},{"raw_affiliation_string":"Aizoon Consulting S.r.l, Turin","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016384155","display_name":"Alexander Tuzhilin","orcid":"https://orcid.org/0000-0003-3354-8462"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Tuzhilin","raw_affiliation_strings":["Stern School of Business, New York University, NY, USA","Stern Business School, New York University, New York,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stern School of Business, New York University, NY, USA","institution_ids":["https://openalex.org/I57206974"]},{"raw_affiliation_string":"Stern Business School, New York University, New York,","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042010777","display_name":"Michele Gorgoglione","orcid":null},"institutions":[{"id":"https://openalex.org/I68618741","display_name":"Polytechnic University of Bari","ror":"https://ror.org/03c44v465","country_code":"IT","type":"education","lineage":["https://openalex.org/I68618741"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"M. Gorgoglione","raw_affiliation_strings":["Dipartimento di Ingegneria Meccanica e Gestionale (DIMEG), Polytechnic of Bari, Bari, Italy","Politechnic of Bari, Bari#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dipartimento di Ingegneria Meccanica e Gestionale (DIMEG), Polytechnic of Bari, Bari, Italy","institution_ids":["https://openalex.org/I68618741"]},{"raw_affiliation_string":"Politechnic of Bari, Bari#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.8866,"has_fulltext":false,"cited_by_count":200,"citation_normalized_percentile":{"value":0.99084883,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"20","issue":"11","first_page":"1535","last_page":"1549"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9969000220298767,"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":0.9969000220298767,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9952999949455261,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.8848868608474731},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7917941808700562},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7102786898612976},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.5431455969810486},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5418863892555237},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5073520541191101},{"id":"https://openalex.org/keywords/contextual-design","display_name":"Contextual design","score":0.4461098611354828},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3453184962272644},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.34117060899734497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3097376823425293},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.2914464473724365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24679279327392578}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8848868608474731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7917941808700562},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7102786898612976},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.5431455969810486},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5418863892555237},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5073520541191101},{"id":"https://openalex.org/C71611378","wikidata":"https://www.wikidata.org/wiki/Q5165191","display_name":"Contextual design","level":3,"score":0.4461098611354828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3453184962272644},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.34117060899734497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3097376823425293},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2914464473724365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24679279327392578},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tkde.2008.110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2008.110","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.421.1420","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.1420","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://pages.stern.nyu.edu/~atuzhili/pdf/TKDE-08-11-final.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W12748326","https://openalex.org/W109903335","https://openalex.org/W218224233","https://openalex.org/W255396808","https://openalex.org/W1510817841","https://openalex.org/W1522660585","https://openalex.org/W1540504152","https://openalex.org/W1546647560","https://openalex.org/W1549854018","https://openalex.org/W1560315852","https://openalex.org/W1563830261","https://openalex.org/W1568392400","https://openalex.org/W1749014589","https://openalex.org/W1790445083","https://openalex.org/W1885064655","https://openalex.org/W1909897245","https://openalex.org/W1955600018","https://openalex.org/W1976093223","https://openalex.org/W2017231306","https://openalex.org/W2023980991","https://openalex.org/W2024486844","https://openalex.org/W2041529939","https://openalex.org/W2043200638","https://openalex.org/W2068427777","https://openalex.org/W2086051332","https://openalex.org/W2099620440","https://openalex.org/W2112430581","https://openalex.org/W2122517148","https://openalex.org/W2123584592","https://openalex.org/W2127235789","https://openalex.org/W2137304183","https://openalex.org/W2149631778","https://openalex.org/W2155912844","https://openalex.org/W2163419627","https://openalex.org/W2213079558","https://openalex.org/W3158586156","https://openalex.org/W4244943366","https://openalex.org/W4285719527","https://openalex.org/W6609564928","https://openalex.org/W6630582740","https://openalex.org/W6633632510","https://openalex.org/W6634009884","https://openalex.org/W6639823891","https://openalex.org/W6679185913"],"related_works":["https://openalex.org/W3217245098","https://openalex.org/W4318612353","https://openalex.org/W2578118947","https://openalex.org/W4390939596","https://openalex.org/W4389829534","https://openalex.org/W2170974669","https://openalex.org/W2586988759","https://openalex.org/W2008630163","https://openalex.org/W2596633139","https://openalex.org/W3199933899"],"abstract_inverted_index":{"The":[0,82],"idea":[1],"that":[2,86,98,120,150],"context":[3,87,105,129,165],"is":[4,53,68,100,117,130],"important":[5,49],"when":[6,54,63,90],"predicting":[7,55],"customer":[8,34,56,65],"behavior":[9,57,93,173],"has":[10,39],"been":[11,40],"maintained":[12],"by":[13,70],"scholars":[14],"in":[15,32,36,113,135,155],"marketing":[16],"and":[17,58,97,146,157,174],"data":[18,109,144],"mining.":[19],"However,":[20],"no":[21],"systematic":[22],"study":[23,47,74],"measuring":[24],"how":[25,48,59],"much":[26],"the":[27,50,92,104,107,128,176],"contextual":[28,51,151,180],"information":[29,52,152],"really":[30],"matters":[31],"building":[33,64],"models":[35],"personalization":[37,156],"applications":[38],"done":[41,69],"before.":[42],"In":[43],"this":[44,136],"paper,":[45],"we":[46],"to":[60,102,162],"use":[61],"it":[62,99],"models.":[66],"It":[67,116],"conducting":[71],"an":[72],"empirical":[73],"across":[75],"a":[76],"wide":[77],"range":[78],"of":[79,94,171,178],"experimental":[80,83],"conditions.":[81],"results":[84],"show":[85,149],"does":[88,153],"matter":[89,154],"modeling":[91],"individual":[95],"customers":[96],"possible":[101],"infer":[103],"from":[106],"existing":[108],"with":[110],"reasonable":[111],"accuracy":[112],"certain":[114],"cases.":[115],"also":[118],"shown":[119],"significant":[121,141],"performance":[122,170],"improvements":[123],"can":[124],"be":[125],"achieved":[126],"if":[127],"\"cleverly\"":[131],"modeled,":[132],"as":[133],"described":[134],"paper.":[137],"These":[138],"findings":[139],"have":[140,159],"implications":[142],"for":[143,167],"miners":[145],"marketers.":[147],"They":[148],"companies":[158],"different":[160],"opportunities":[161],"both":[163],"make":[164],"valuable":[166],"improving":[168],"predictive":[169],"customers'":[172],"decreasing":[175],"costs":[177],"gathering":[179],"information.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":17},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":17},{"year":2013,"cited_by_count":26},{"year":2012,"cited_by_count":22}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
