{"id":"https://openalex.org/W3036699618","doi":"https://doi.org/10.1109/tmc.2020.3003542","title":"NCF: A Neural Context Fusion Approach to Raw Mobility Annotation","display_name":"NCF: A Neural Context Fusion Approach to Raw Mobility Annotation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3036699618","doi":"https://doi.org/10.1109/tmc.2020.3003542","mag":"3036699618"},"language":"en","primary_location":{"id":"doi:10.1109/tmc.2020.3003542","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2020.3003542","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"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 Mobile Computing","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/A5103128873","display_name":"Renjun Hu","orcid":"https://orcid.org/0000-0002-1094-6890"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renjun Hu","raw_affiliation_strings":["SKLSDE Lab, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"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, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China","institution_ids":["https://openalex.org/I4210129579","https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047229109","display_name":"Xinjiang Lu","orcid":"https://orcid.org/0000-0002-3602-0391"},"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":"Xinjiang Lu","raw_affiliation_strings":["Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China","institution_ids":["https://openalex.org/I4210129579","https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049015446","display_name":"Hengshu Zhu","orcid":"https://orcid.org/0000-0003-4570-643X"},"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":"Hengshu Zhu","raw_affiliation_strings":["Talent Intelligence Center, Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Talent Intelligence Center, Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006980420","display_name":"Shuai Ma","orcid":"https://orcid.org/0000-0002-4050-0443"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Ma","raw_affiliation_strings":["SKLSDE Lab, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SKLSDE Lab, Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Management Science and Information Systems Department, Rutgers Business School, Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Management Science and Information Systems Department, Rutgers Business School, Rutgers University, Newark, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8685,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81878814,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"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":1.0,"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":1.0,"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/T11106","display_name":"Data Management and Algorithms","score":0.9905999898910522,"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"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9887999892234802,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8777786493301392},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6446629762649536},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5327116847038269},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5015559196472168},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4913546144962311},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.48840683698654175},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.478076696395874},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.45272472500801086},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4313937723636627},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42429226636886597},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.4208253026008606},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33272403478622437},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10031014680862427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8777786493301392},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6446629762649536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5327116847038269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5015559196472168},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4913546144962311},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.48840683698654175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.478076696395874},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.45272472500801086},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4313937723636627},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42429226636886597},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.4208253026008606},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33272403478622437},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10031014680862427},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2020.3003542","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2020.3003542","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"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 Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4453379053","display_name":null,"funder_award_id":"61925203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5152102630","display_name":null,"funder_award_id":"U1636210","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5415502266","display_name":null,"funder_award_id":"71531001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8055127673","display_name":null,"funder_award_id":"61421003","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":40,"referenced_works":["https://openalex.org/W1965499304","https://openalex.org/W1982300822","https://openalex.org/W1984189333","https://openalex.org/W1987228002","https://openalex.org/W1990444226","https://openalex.org/W2011968383","https://openalex.org/W2012084072","https://openalex.org/W2012580531","https://openalex.org/W2026532078","https://openalex.org/W2035949918","https://openalex.org/W2071702404","https://openalex.org/W2095705004","https://openalex.org/W2101823987","https://openalex.org/W2111051703","https://openalex.org/W2167686542","https://openalex.org/W2358962606","https://openalex.org/W2385600359","https://openalex.org/W2406819242","https://openalex.org/W2508716390","https://openalex.org/W2534727297","https://openalex.org/W2537003471","https://openalex.org/W2539781657","https://openalex.org/W2556289220","https://openalex.org/W2563735978","https://openalex.org/W2585077751","https://openalex.org/W2743808405","https://openalex.org/W2749061957","https://openalex.org/W2760942449","https://openalex.org/W2788029981","https://openalex.org/W2788114581","https://openalex.org/W2789309090","https://openalex.org/W2846263287","https://openalex.org/W2868921360","https://openalex.org/W2904403013","https://openalex.org/W2964308564","https://openalex.org/W2982112359","https://openalex.org/W3105196786","https://openalex.org/W6674330103","https://openalex.org/W6679434410","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W17155033","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W3207760230","https://openalex.org/W2368049389","https://openalex.org/W1496222301","https://openalex.org/W2384861574","https://openalex.org/W2170801710"],"abstract_inverted_index":{"Understanding":[0],"human":[1,56,195],"mobility":[2,39,57,74,196],"patterns":[3],"at":[4,180],"the":[5,37,41,68,95,112,156,176,189,192],"point-of-interest":[6],"(POI)":[7],"scale":[8],"plays":[9],"an":[10,106],"important":[11],"role":[12],"in":[13,17,26,60,88,115,184],"enhancing":[14],"business":[15],"intelligence":[16],"mobile":[18],"environments.":[19],"While":[20],"large":[21],"efforts":[22],"have":[23],"been":[24],"made":[25],"this":[27,61],"direction,":[28],"most":[29],"studies":[30],"simply":[31],"utilize":[32],"POI":[33,199],"check-ins":[34],"to":[35,49,65,109,154],"mine":[36],"concerned":[38],"patterns,":[40],"effectiveness":[42],"of":[43,191],"which":[44,83],"is":[45],"usually":[46],"hindered":[47],"due":[48],"data":[50,162],"sparsity.":[51],"To":[52],"obtain":[53],"better":[54],"POI-based":[55,194],"for":[58],"mining,":[59],"paper,":[62],"we":[63,104,149,164,187],"strive":[64],"directly":[66],"annotate":[67],"POIs":[69],"associated":[70],"with":[71,111,143,197],"raw":[72,116],"user-generated":[73],"records.":[75],"We":[76],"propose":[77],"a":[78,136,144,151,198],"neural":[79,146],"context":[80,86],"fusion":[81],"approach":[82,93,131,173],"integrates":[84],"various":[85],"factors":[87,99],"people's":[89],"POI-visiting":[90],"behaviors.":[91],"Our":[92],"evaluates":[94],"preference":[96],"and":[97,125,129,169],"transition":[98],"via":[100],"representation":[101],"learning.":[102],"Notably,":[103],"incorporate":[105],"attention":[107],"mechanism":[108],"deal":[110],"randomized":[113],"transitions":[114],"mobility.":[117],"The":[118],"domain":[119],"knowledge":[120],"factors,":[121],"i.e.,":[122],"distance,":[123],"time":[124],"popularity,":[126],"remain":[127],"effective":[128],"our":[130,166,172],"further":[132],"includes":[133],"them":[134],"from":[135],"data-driven":[137],"perspective.":[138],"Factors":[139],"are":[140],"automatically":[141],"fused":[142],"feed-forward":[145],"network.":[147],"Furthermore,":[148],"exploit":[150],"multi-head":[152],"architecture":[153],"enhance":[155],"model":[157],"expressiveness.":[158],"Using":[159],"two":[160],"real-life":[161],"sets,":[163],"conduct":[165],"experimental":[167],"study":[168],"find":[170],"that":[171],"consistently":[174],"outperforms":[175],"state-of-the-art":[177],"baselines":[178],"by":[179],"least":[181],"32":[182],"percent":[183],"accuracy.":[185],"Besides,":[186],"demonstrate":[188],"utility":[190],"obtained":[193],"recommendation":[200],"example.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}
