{"id":"https://openalex.org/W3032980606","doi":"https://doi.org/10.1109/tkde.2020.3000287","title":"A Unified Framework for User Identification Across Online and Offline Data","display_name":"A Unified Framework for User Identification Across Online and Offline Data","publication_year":2020,"publication_date":"2020-06-05","ids":{"openalex":"https://openalex.org/W3032980606","doi":"https://doi.org/10.1109/tkde.2020.3000287","mag":"3032980606"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2020.3000287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3000287","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/A5017813678","display_name":"Tianyi Hao","orcid":"https://orcid.org/0000-0003-0221-4873"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Hao","raw_affiliation_strings":["Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0221-4873","affiliations":[{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101481194","display_name":"Jingbo Zhou","orcid":"https://orcid.org/0000-0003-2677-7021"},"institutions":[{"id":"https://openalex.org/I4210129579","display_name":"National Engineering Laboratory of Deep Learning Technology and Application","ror":"https://ror.org/03z8p5796","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210129579"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingbo Zhou","raw_affiliation_strings":["Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing 100085, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing 100085, China","institution_ids":["https://openalex.org/I4210129579","https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112842756","display_name":"Yunsheng Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunsheng Cheng","raw_affiliation_strings":["Big Data Lab, Baidu Research, Beijing 100085, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Lab, Baidu Research, Beijing 100085, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082905458","display_name":"Longbo Huang","orcid":"https://orcid.org/0000-0002-7341-447X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longbo Huang","raw_affiliation_strings":["Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012284260","display_name":"Haishan Wu","orcid":"https://orcid.org/0000-0002-6571-7024"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haishan Wu","raw_affiliation_strings":["Big Data Lab, Baidu Research, Beijing 100085, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Lab, Baidu Research, Beijing 100085, China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8148,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.87272445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"34","issue":"4","first_page":"1562","last_page":"1575"},"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":0.9983000159263611,"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":0.9983000159263611,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.882007896900177},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6151371002197266},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.595901370048523},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5772284269332886},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.48764288425445557},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4309350848197937},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.42738014459609985},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.42052149772644043},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.331927090883255},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.29751163721084595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24892783164978027}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.882007896900177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6151371002197266},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.595901370048523},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5772284269332886},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.48764288425445557},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4309350848197937},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.42738014459609985},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.42052149772644043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.331927090883255},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29751163721084595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24892783164978027},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2020.3000287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3000287","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G72957147","display_name":null,"funder_award_id":"61672316","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W812851569","https://openalex.org/W1575736368","https://openalex.org/W1658960529","https://openalex.org/W1673310716","https://openalex.org/W1833521484","https://openalex.org/W1967419472","https://openalex.org/W1982300822","https://openalex.org/W2017481266","https://openalex.org/W2026910242","https://openalex.org/W2036605328","https://openalex.org/W2045686369","https://openalex.org/W2047532797","https://openalex.org/W2055345291","https://openalex.org/W2097776316","https://openalex.org/W2104599107","https://openalex.org/W2115240023","https://openalex.org/W2133591726","https://openalex.org/W2149427297","https://openalex.org/W2167686542","https://openalex.org/W2168175751","https://openalex.org/W2169896292","https://openalex.org/W2170379762","https://openalex.org/W2173213060","https://openalex.org/W2182891243","https://openalex.org/W2195256693","https://openalex.org/W2235660311","https://openalex.org/W2241660235","https://openalex.org/W2295598076","https://openalex.org/W2295752535","https://openalex.org/W2339803498","https://openalex.org/W2426948338","https://openalex.org/W2488678869","https://openalex.org/W2517957495","https://openalex.org/W2561333426","https://openalex.org/W3099827099","https://openalex.org/W4241186228"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2942366970","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2562400057","https://openalex.org/W1692008701","https://openalex.org/W2597588799","https://openalex.org/W4360593462","https://openalex.org/W2194570607"],"abstract_inverted_index":{"User":[0],"identification":[1,35,80],"across":[2,47],"multiple":[3,48,167],"datasets":[4,63],"has":[5,13],"a":[6,37,45,57,76,96,110,120,141,159],"wide":[7],"range":[8],"of":[9,18,29,93,114,126,166,191,200],"applications":[10],"and":[11,54,83,149,189,198],"there":[12],"been":[14],"an":[15],"increasing":[16],"set":[17],"research":[19],"works":[20,31],"on":[21,33,78,124,177],"this":[22,72,106],"topic":[23],"during":[24],"recent":[25],"years.":[26],"However,":[27],"most":[28],"existing":[30],"focus":[32],"user":[34,46,59,79],"with":[36,51,64,195],"single":[38,58],"input":[39],"data":[40,53,94],"type,":[41],"e.g.,":[42],"(I)":[43],"identifying":[44],"social":[49],"networks":[50],"online":[52,82],"(II)":[55],"detecting":[56],"from":[60,68,98],"heterogeneous":[61],"trajectory":[62],"offline":[65,84],"data.":[66],"Different":[67],"previous":[69],"works,":[70],"in":[71,171],"paper,":[73],"we":[74,108,118,139,157,169,180],"propose":[75,109,140],"framework":[77,112],"between":[81,89],"datasets.":[85],"We":[86],"build":[87],"connections":[88],"these":[90],"two":[91,174],"types":[92],"by":[95],"mapping":[97],"IP":[99,127,131],"addresses":[100,128,132],"to":[101,129,145,162,183,204],"physical":[102,135],"locations.":[103],"To":[104],"solve":[105],"problem,":[107],"novel":[111,142],"consisting":[113],"three":[115],"steps.":[116,175],"First,":[117],"use":[119],"clustering":[121],"method":[122,161],"based":[123],"locations":[125],"map":[130],"into":[133],"specific":[134],"location":[136],"distributions.":[137],"Second,":[138],"pairwise":[143],"index":[144],"reduce":[146],"space":[147],"cost":[148],"running":[150],"time":[151,188],"for":[152],"computing":[153],"the":[154,164,172,185,196],"co-occurrence.":[155],"Lastly,":[156],"apply":[158],"learning-to-rank":[160],"merge":[163],"effect":[165],"features":[168],"get":[170],"first":[173],"Based":[176],"our":[178,192,201],"framework,":[179,193],"design":[181],"experiments":[182],"demonstrate":[184],"efficiency":[186],"(in":[187],"space)":[190],"together":[194],"precision":[197],"recall":[199],"approach":[202],"compared":[203],"other":[205],"methods.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
