{"id":"https://openalex.org/W2330318528","doi":"https://doi.org/10.1109/tkde.2016.2550436","title":"A General Multi-Context Embedding Model for Mining Human Trajectory Data","display_name":"A General Multi-Context Embedding Model for Mining Human Trajectory Data","publication_year":2016,"publication_date":"2016-04-05","ids":{"openalex":"https://openalex.org/W2330318528","doi":"https://doi.org/10.1109/tkde.2016.2550436","mag":"2330318528"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2016.2550436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2016.2550436","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/A5086620268","display_name":"Ningnan Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ningnan Zhou","raw_affiliation_strings":["School of Information, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037145565","display_name":"Wayne Xin Zhao","orcid":"https://orcid.org/0000-0002-8333-6196"},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]},{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wayne Xin Zhao","raw_affiliation_strings":["Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China","School of Information, Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China","institution_ids":["https://openalex.org/I4210096250"]},{"raw_affiliation_string":"School of Information, Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320847","display_name":"Xiao Zhang","orcid":"https://orcid.org/0000-0001-7397-5632"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Zhang","raw_affiliation_strings":["School of Information, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025631695","display_name":"Ji-Rong Wen","orcid":"https://orcid.org/0000-0002-9777-9676"},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]},{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji-Rong Wen","raw_affiliation_strings":["Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China","School of Information, Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China","institution_ids":["https://openalex.org/I4210096250"]},{"raw_affiliation_string":"School of Information, Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100388415","display_name":"Shan Wang","orcid":"https://orcid.org/0000-0001-7676-3424"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Wang","raw_affiliation_strings":["School of Information, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5086620268"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":11.83395463,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.99266716,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"28","issue":"8","first_page":"1945","last_page":"1958"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9900000095367432,"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":0.8423244953155518},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8383534550666809},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6937112808227539},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6492314338684082},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6315128207206726},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4541640877723694},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4518088102340698},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4254401922225952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4187876880168915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4187546968460083},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3323916792869568},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.07149839401245117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8423244953155518},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8383534550666809},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6937112808227539},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6492314338684082},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6315128207206726},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4541640877723694},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4518088102340698},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4254401922225952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4187876880168915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4187546968460083},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3323916792869568},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.07149839401245117},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2016.2550436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2016.2550436","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/G3905394689","display_name":null,"funder_award_id":"4162032","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G5859226697","display_name":null,"funder_award_id":"61502502","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"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W36903255","https://openalex.org/W1498436455","https://openalex.org/W1546409232","https://openalex.org/W1592410058","https://openalex.org/W1599541430","https://openalex.org/W1608194207","https://openalex.org/W1614298861","https://openalex.org/W1676985236","https://openalex.org/W1967635926","https://openalex.org/W1972243012","https://openalex.org/W1972436494","https://openalex.org/W1982397092","https://openalex.org/W1985854669","https://openalex.org/W2007321142","https://openalex.org/W2012580531","https://openalex.org/W2013315566","https://openalex.org/W2017921654","https://openalex.org/W2023279748","https://openalex.org/W2033626772","https://openalex.org/W2054560962","https://openalex.org/W2057991616","https://openalex.org/W2064702560","https://openalex.org/W2072841881","https://openalex.org/W2073013176","https://openalex.org/W2073021764","https://openalex.org/W2073601450","https://openalex.org/W2075190119","https://openalex.org/W2079272365","https://openalex.org/W2086142729","https://openalex.org/W2087692915","https://openalex.org/W2101108259","https://openalex.org/W2110953678","https://openalex.org/W2120861206","https://openalex.org/W2126895033","https://openalex.org/W2130354913","https://openalex.org/W2131744502","https://openalex.org/W2133400794","https://openalex.org/W2134946452","https://openalex.org/W2138204974","https://openalex.org/W2141136363","https://openalex.org/W2144685566","https://openalex.org/W2146232090","https://openalex.org/W2147694185","https://openalex.org/W2149814409","https://openalex.org/W2151802055","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2156404870","https://openalex.org/W2158454296","https://openalex.org/W2162931300","https://openalex.org/W2166692930","https://openalex.org/W2167686542","https://openalex.org/W2189936406","https://openalex.org/W2329660289","https://openalex.org/W2405496393","https://openalex.org/W2408538552","https://openalex.org/W2996402732","https://openalex.org/W3104097132","https://openalex.org/W4254829975","https://openalex.org/W4285719527","https://openalex.org/W6635857930","https://openalex.org/W6636510571","https://openalex.org/W6678040779","https://openalex.org/W6680106237","https://openalex.org/W6687290802"],"related_works":["https://openalex.org/W2032233321","https://openalex.org/W3121970507","https://openalex.org/W2110028391","https://openalex.org/W2081900870","https://openalex.org/W54497855","https://openalex.org/W217960748","https://openalex.org/W3125814499","https://openalex.org/W2090827041","https://openalex.org/W2565703248","https://openalex.org/W187246281"],"abstract_inverted_index":{"The":[0],"proliferation":[1],"of":[2,15,35,102,111,135,189],"location-based":[3],"social":[4,171],"networks,":[5],"such":[6],"as":[7],"Foursquare":[8],"and":[9,28,46,94,146,170],"Facebook":[10],"Places,":[11],"offers":[12],"a":[13,71,82],"variety":[14],"ways":[16],"to":[17,38,44,51,78,98,126,163],"record":[18],"human":[19,57],"mobility,":[20,58],"including":[21,142],"user":[22],"generated":[23],"geo-tagged":[24],"contents,":[25],"check-in":[26],"services,":[27],"mobile":[29],"apps.":[30],"Although":[31],"trajectory":[32,48,127,136],"data":[33,49,137],"is":[34,42,60,86,96,115,153],"great":[36],"value":[37],"many":[39],"applications,":[40],"it":[41,95,114],"challenging":[43,165],"analyze":[45],"mine":[47],"due":[50],"the":[52,89,109,116,120,139,150,156,187],"complex":[53],"characteristics":[54],"reflected":[55],"in":[56,81,88,155],"which":[59],"affected":[61],"by":[62],"multiple":[63,132],"contextual":[64],"information.":[65],"In":[66],"this":[67],"paper,":[68],"we":[69],"propose":[70],"Multi-Context":[72],"Trajectory":[73],"Embedding":[74],"Model,":[75],"called":[76],"MC-TEM,":[77],"explore":[79],"contexts":[80,104],"systematic":[83],"way.":[84],"MC-TEM":[85,162,191],"developed":[87],"distributed":[90,121],"representation":[91,122],"learning":[92,123],"framework,":[93],"flexible":[97],"characterize":[99],"various":[100],"kinds":[101],"useful":[103],"for":[105],"different":[106],"applications.":[107],"To":[108],"best":[110],"our":[112,190],"knowledge,":[113],"first":[117],"time":[118],"that":[119],"methods":[124],"apply":[125,161],"data.":[128],"We":[129,160,174],"formally":[130],"incorporate":[131],"context":[133,151],"information":[134,152],"into":[138],"proposed":[140],"model,":[141],"user-level,":[143],"trajectory-level,":[144],"location-level,":[145],"temporal":[147],"contexts.":[148],"All":[149],"represented":[154],"same":[157],"embedding":[158],"space.":[159],"two":[164],"tasks,":[166],"namely":[167],"location":[168],"recommendation":[169],"link":[172],"prediction.":[173],"conduct":[175],"extensive":[176],"experiments":[177],"on":[178],"three":[179],"real-world":[180],"datasets.":[181],"Extensive":[182],"experiment":[183],"results":[184],"have":[185],"demonstrated":[186],"superiority":[188],"model":[192],"over":[193],"several":[194],"state-of-the-art":[195],"methods.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
