{"id":"https://openalex.org/W4312239160","doi":"https://doi.org/10.1109/access.2022.3215732","title":"User Demographic Prediction Based on the Fusion of Mobile and Survey Data","display_name":"User Demographic Prediction Based on the Fusion of Mobile and Survey Data","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312239160","doi":"https://doi.org/10.1109/access.2022.3215732"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3215732","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215732","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09923905.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09923905.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100387346","display_name":"Xingyu Chen","orcid":"https://orcid.org/0000-0002-6729-2277"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyu Chen","raw_affiliation_strings":["Department of User and Market Research, China Mobile Research Institute, Beijing, China","Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China"],"raw_orcid":"https://orcid.org/0000-0002-6729-2277","affiliations":[{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088622196","display_name":"Ye Guo","orcid":"https://orcid.org/0000-0001-6841-9359"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Guo","raw_affiliation_strings":["Department of User and Market Research, China Mobile Research Institute, Beijing, China","Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China"],"raw_orcid":"https://orcid.org/0000-0001-6841-9359","affiliations":[{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100304379","display_name":"Honglei Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honglei Xu","raw_affiliation_strings":["Department of User and Market Research, China Mobile Research Institute, Beijing, China","Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101399735","display_name":"Hongyan Yan","orcid":"https://orcid.org/0000-0001-9560-7136"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Yan","raw_affiliation_strings":["Department of User and Market Research, China Mobile Research Institute, Beijing, China","Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100342079","display_name":"Lin Lin","orcid":"https://orcid.org/0000-0003-3505-0465"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Lin","raw_affiliation_strings":["Department of User and Market Research, China Mobile Research Institute, Beijing, China","Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]},{"raw_affiliation_string":"Department of User and Market Research, China Mobile Research Institute, Beijing 100032, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7728,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.79047305,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"10","issue":null,"first_page":"111507","last_page":"111527"},"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":0.9977999925613403,"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":0.9977999925613403,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9945999979972839,"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/computer-science","display_name":"Computer science","score":0.8248829245567322},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5987757444381714},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5819473266601562},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5737717151641846},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5496727228164673},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4392126798629761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43687868118286133},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43421363830566406},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42624136805534363},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0880969762802124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8248829245567322},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5987757444381714},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5819473266601562},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5737717151641846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5496727228164673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392126798629761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43687868118286133},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43421363830566406},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42624136805534363},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0880969762802124},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3215732","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215732","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09923905.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6c5f01aaacd44fedad62eeee38dedd1e","is_oa":true,"landing_page_url":"https://doaj.org/article/6c5f01aaacd44fedad62eeee38dedd1e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 111507-111527 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3215732","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3215732","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09923905.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312239160.pdf","grobid_xml":"https://content.openalex.org/works/W4312239160.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W1977492702","https://openalex.org/W2062051372","https://openalex.org/W2065130322","https://openalex.org/W2076027458","https://openalex.org/W2076618162","https://openalex.org/W2112257737","https://openalex.org/W2158698691","https://openalex.org/W2167001236","https://openalex.org/W2173315138","https://openalex.org/W2179234838","https://openalex.org/W2286737780","https://openalex.org/W2475334473","https://openalex.org/W2506888547","https://openalex.org/W2509235963","https://openalex.org/W2511316824","https://openalex.org/W2528628564","https://openalex.org/W2604662567","https://openalex.org/W2605367353","https://openalex.org/W2616068566","https://openalex.org/W2619062098","https://openalex.org/W2624392389","https://openalex.org/W2723293840","https://openalex.org/W2759505905","https://openalex.org/W2763913564","https://openalex.org/W2782929953","https://openalex.org/W2786512490","https://openalex.org/W2793512971","https://openalex.org/W2805153331","https://openalex.org/W2893290937","https://openalex.org/W2893649452","https://openalex.org/W2896018557","https://openalex.org/W2900060587","https://openalex.org/W2902827029","https://openalex.org/W2903746889","https://openalex.org/W2916062148","https://openalex.org/W2921252114","https://openalex.org/W2933213120","https://openalex.org/W2939804553","https://openalex.org/W2950425960","https://openalex.org/W2955594526","https://openalex.org/W2962745591","https://openalex.org/W2963953172","https://openalex.org/W2964037239","https://openalex.org/W2964341035","https://openalex.org/W2971368946","https://openalex.org/W2971678316","https://openalex.org/W2997842691","https://openalex.org/W3005378242","https://openalex.org/W3033237746","https://openalex.org/W3081430124","https://openalex.org/W3124316558","https://openalex.org/W3125401430","https://openalex.org/W3125669903","https://openalex.org/W3137484374","https://openalex.org/W3155307902","https://openalex.org/W3164006073","https://openalex.org/W3171874185","https://openalex.org/W3209943551","https://openalex.org/W4225524297","https://openalex.org/W4239510810","https://openalex.org/W4291148931","https://openalex.org/W6602840736","https://openalex.org/W6682551307","https://openalex.org/W6739901393","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W2101155126","https://openalex.org/W2043093291","https://openalex.org/W2363545964"],"abstract_inverted_index":{"The":[0],"user":[1,15,89,118],"demographic":[2,90],"prediction":[3,91,175],"problem":[4,35],"is":[5,18],"one":[6,142],"of":[7,14,19,29,58,128,177],"the":[8,12,30,68,74,126,129,132,174,189],"critical":[9],"processes":[10],"in":[11,54,62],"construction":[13],"profiles,":[16],"which":[17,65],"great":[20,56],"significance":[21],"for":[22,88],"understanding":[23],"users\u2019":[24,151,161],"characteristics":[25],"and":[26,60,95,98,131,163,179,185],"attributes.":[27],"Most":[28],"prior":[31],"works":[32],"on":[33,93,135,170],"this":[34,83],"either":[36],"used":[37],"only":[38],"single-source":[39],"data":[40,59,114],"or":[41],"employed":[42],"a":[43,55,86,100,136,146],"hard-matching":[44],"method":[45],"to":[46,79,112,159,183,188],"handle":[47],"multi-source":[48],"data.":[49],"These":[50],"methods":[51],"will":[52],"result":[53],"loss":[57],"information":[61,157],"many":[63],"circumstances,":[64],"may":[66],"affect":[67],"model\u2019s":[69],"accuracy":[70,176],"as":[71,73,154],"well":[72],"application":[75],"scenarios.":[76],"In":[77],"order":[78],"solve":[80],"these":[81,171],"problems,":[82],"paper":[84],"proposes":[85],"framework":[87,130,166],"based":[92],"mobile":[94,138],"survey":[96,147],"data,":[97],"presents":[99],"Deep":[101],"Structured":[102],"Fusion":[103],"Model":[104],"(DSFM)":[105],"using":[106,145],"neural":[107],"networks":[108],"with":[109,140],"attention":[110],"mechanisms":[111],"perform":[113],"fusion":[115,133],"by":[116,181],"comparing":[117],"similarity":[119],"between":[120],"two":[121],"heterogeneous":[122],"datasets.":[123],"We":[124],"examine":[125],"effectiveness":[127],"model":[134],"real-world":[137],"dataset":[139,148],"almost":[141],"billion":[143],"users,":[144],"containing":[149],"29,809":[150],"questionnaire":[152],"results":[153,169],"an":[155],"additional":[156],"source":[158],"predict":[160],"age":[162,180],"gender.":[164],"Our":[165],"achieves":[167],"excellent":[168],"datasets,":[172],"increasing":[173],"gender":[178],"up":[182],"3.23%":[184],"5.21%":[186],"compared":[187],"best":[190],"baseline":[191],"model.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
