{"id":"https://openalex.org/W2979111891","doi":"https://doi.org/10.1109/ijcnn.2019.8851888","title":"Using Deep Learning for Mobile Marketing User Conversion Prediction","display_name":"Using Deep Learning for Mobile Marketing User Conversion Prediction","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2979111891","doi":"https://doi.org/10.1109/ijcnn.2019.8851888","mag":"2979111891"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5052355746","display_name":"Lu\u00eds Miguel Matos","orcid":"https://orcid.org/0000-0001-5827-9129"},"institutions":[{"id":"https://openalex.org/I99682543","display_name":"University of Minho","ror":"https://ror.org/037wpkx04","country_code":"PT","type":"education","lineage":["https://openalex.org/I99682543"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Luis Miguel Matos","raw_affiliation_strings":["ALGORITMI Centre, University of Minho, Guimar\u00e3es, Portugal"],"affiliations":[{"raw_affiliation_string":"ALGORITMI Centre, University of Minho, Guimar\u00e3es, Portugal","institution_ids":["https://openalex.org/I99682543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077829125","display_name":"Paulo Cortez","orcid":"https://orcid.org/0000-0002-7991-2090"},"institutions":[{"id":"https://openalex.org/I99682543","display_name":"University of Minho","ror":"https://ror.org/037wpkx04","country_code":"PT","type":"education","lineage":["https://openalex.org/I99682543"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Paulo Cortez","raw_affiliation_strings":["ALGORITMI Centre, University of Minho, Guimar\u00e3es, Portugal"],"affiliations":[{"raw_affiliation_string":"ALGORITMI Centre, University of Minho, Guimar\u00e3es, Portugal","institution_ids":["https://openalex.org/I99682543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081140606","display_name":"Rui Mendes","orcid":"https://orcid.org/0000-0002-5321-6863"},"institutions":[{"id":"https://openalex.org/I99682543","display_name":"University of Minho","ror":"https://ror.org/037wpkx04","country_code":"PT","type":"education","lineage":["https://openalex.org/I99682543"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Rui Mendes","raw_affiliation_strings":["ALGORITMI Centre, University of Minho, Braga, Portugal"],"affiliations":[{"raw_affiliation_string":"ALGORITMI Centre, University of Minho, Braga, Portugal","institution_ids":["https://openalex.org/I99682543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101840381","display_name":"Antoine Moreau","orcid":"https://orcid.org/0000-0001-8226-6715"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antoine Moreau","raw_affiliation_strings":["OLAmobile, Guimar\u00e3es, Portugal"],"affiliations":[{"raw_affiliation_string":"OLAmobile, Guimar\u00e3es, Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052355746"],"corresponding_institution_ids":["https://openalex.org/I99682543"],"apc_list":null,"apc_paid":null,"fwci":3.2578,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.91119675,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9976000189781189,"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"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9976000189781189,"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"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.991599977016449,"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":0.8117918968200684},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6398028135299683},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6258718371391296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5940828919410706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5508789420127869},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5222983360290527},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5117619037628174},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4989750385284424},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.452256441116333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4423038363456726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8117918968200684},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6398028135299683},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6258718371391296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5940828919410706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5508789420127869},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5222983360290527},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5117619037628174},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4989750385284424},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.452256441116333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4423038363456726},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:repositorium.sdum.uminho.pt:1822/62771","is_oa":false,"landing_page_url":"http://hdl.handle.net/1822/62771","pdf_url":null,"source":{"id":"https://openalex.org/S4306400354","display_name":"Reposit\u00f3riUM (Universidade do Minho)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99682543","host_organization_name":"University of Minho","host_organization_lineage":["https://openalex.org/I99682543"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"},{"id":"pmh:oai:repositorium.uminho.pt:1822/62771","is_oa":false,"landing_page_url":"https://hdl.handle.net/1822/62771","pdf_url":null,"source":{"id":"https://openalex.org/S4306402433","display_name":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W196999659","https://openalex.org/W1483176838","https://openalex.org/W1578517626","https://openalex.org/W1993220166","https://openalex.org/W2042506099","https://openalex.org/W2060204748","https://openalex.org/W2130486630","https://openalex.org/W2158698691","https://openalex.org/W2331120268","https://openalex.org/W2443960221","https://openalex.org/W2558311305","https://openalex.org/W2588336250","https://openalex.org/W2768307941","https://openalex.org/W2796816447","https://openalex.org/W2807401465","https://openalex.org/W2919115771","https://openalex.org/W2964325980","https://openalex.org/W6600284362","https://openalex.org/W6628927740","https://openalex.org/W6718631636","https://openalex.org/W6752208194"],"related_works":["https://openalex.org/W3014300295","https://openalex.org/W4293567684","https://openalex.org/W4311257506","https://openalex.org/W3189515467","https://openalex.org/W2337926734","https://openalex.org/W4319994054","https://openalex.org/W4366224123","https://openalex.org/W2963958939","https://openalex.org/W4320802194","https://openalex.org/W3173182854"],"abstract_inverted_index":{"Mobile":[0],"performance":[1,89],"marketing":[2,58],"is":[3],"a":[4,45,55,61,107,146,151],"growing":[5],"industry":[6],"due":[7],"to":[8,25,37],"the":[9,27,85,91,102,130,134],"massive":[10],"adoption":[11],"of":[12,31],"smartphones":[13],"and":[14,75,90,106,113,120,133,142,150],"tablets.":[15],"In":[16],"this":[17],"paper,":[18],"we":[19,66,82],"explore":[20],"Deep":[21],"Multilayer":[22],"Perceptrons":[23],"(MLP)":[24],"predict":[26],"Conversion":[28],"Rate":[29],"(CVR)":[30],"mobile":[32,57],"users":[33],"that":[34],"are":[35],"redirected":[36],"ad":[38],"campaigns":[39],"(i.e.,":[40],"if":[41],"there":[42],"will":[43],"be":[44],"sale).":[46],"We":[47],"analyze":[48],"recent":[49],"real-world":[50],"big":[51],"data":[52,77,99],"provided":[53],"by":[54,129],"global":[56],"company.":[59],"Using":[60],"realistic":[62],"rolling":[63],"window":[64],"validation,":[65],"conducted":[67],"several":[68],"experiments":[69,96],"with":[70],"different":[71],"datasets":[72],"(two":[73],"sampling":[74],"two":[76,98,114,135],"traffic":[78],"modes),":[79],"in":[80],"which":[81],"measure":[83],"both":[84],"predictive":[86],"binary":[87],"classification":[88,125],"computational":[92],"effort.":[93],"The":[94],"modeling":[95],"include:":[97],"preprocessing":[100],"methods,":[101],"popular":[103],"one-hot":[104],"encoding":[105],"proposed":[108],"Percentage":[109],"Categorical":[110],"Pruning":[111],"(PCP);":[112],"MLP":[115,136],"learning":[116,137],"modes,":[117,138],"offline":[118],"(reset)":[119],"online":[121],"(reuse).":[122],"Overall,":[123],"competitive":[124],"results":[126],"were":[127],"achieved":[128],"PCP":[131],"transform":[132],"producing":[139],"real-time":[140],"predictions":[141],"comparing":[143],"favorably":[144],"against":[145],"Convolutional":[147],"Neural":[148],"Network":[149],"Logistic":[152],"Regression.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}