{"id":"https://openalex.org/W3081189998","doi":"https://doi.org/10.1145/3394486.3403128","title":"Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams","display_name":"Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3081189998","doi":"https://doi.org/10.1145/3394486.3403128","mag":"3081189998"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403128","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403128","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036270316","display_name":"Pengyang Wang","orcid":"https://orcid.org/0000-0003-3961-5523"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pengyang Wang","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100786547","display_name":"Kunpeng Liu","orcid":"https://orcid.org/0000-0002-6053-5977"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunpeng Liu","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003684662","display_name":"Lu Jiang","orcid":"https://orcid.org/0000-0003-4529-8114"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Jiang","raw_affiliation_strings":["Northeast Normal University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Normal University, Changchun, China","institution_ids":["https://openalex.org/I184983240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353947","display_name":"Xiaolin Li","orcid":"https://orcid.org/0000-0002-7728-0157"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Li","raw_affiliation_strings":["Nanjing University, Nanjing, UNK, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, UNK, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032187620","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0002-1767-8024"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":72,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"853","last_page":"861"},"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":0.9991999864578247,"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":0.9991999864578247,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8465312719345093},{"id":"https://openalex.org/keywords/profiling","display_name":"Profiling (computer programming)","score":0.7130871415138245},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6014321446418762},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.49970293045043945},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.46349167823791504},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4584064781665802},{"id":"https://openalex.org/keywords/user-profile","display_name":"User profile","score":0.4454251825809479},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.4409492611885071},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39705246686935425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37234941124916077},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.17907223105430603},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17769208550453186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8465312719345093},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.7130871415138245},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6014321446418762},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.49970293045043945},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.46349167823791504},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4584064781665802},{"id":"https://openalex.org/C2780150774","wikidata":"https://www.wikidata.org/wiki/Q252500","display_name":"User profile","level":2,"score":0.4454251825809479},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4409492611885071},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39705246686935425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37234941124916077},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.17907223105430603},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17769208550453186},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394486.3403128","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403128","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1595483645","https://openalex.org/W1757796397","https://openalex.org/W2017921654","https://openalex.org/W2035949918","https://openalex.org/W2071702404","https://openalex.org/W2088609771","https://openalex.org/W2127795553","https://openalex.org/W2137245235","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2283196293","https://openalex.org/W2512971201","https://openalex.org/W2743289629","https://openalex.org/W2750303327","https://openalex.org/W2787933113","https://openalex.org/W2788295351","https://openalex.org/W2799117832","https://openalex.org/W2799784543","https://openalex.org/W2802983566","https://openalex.org/W2921980263","https://openalex.org/W2949382160","https://openalex.org/W2949704773","https://openalex.org/W2963519394","https://openalex.org/W3034288158","https://openalex.org/W3102778384","https://openalex.org/W3122507327"],"related_works":["https://openalex.org/W2005630130","https://openalex.org/W2132546753","https://openalex.org/W2189007570","https://openalex.org/W2144035188","https://openalex.org/W184756452","https://openalex.org/W1990804090","https://openalex.org/W2766132956","https://openalex.org/W3008307459","https://openalex.org/W2327596978","https://openalex.org/W2976552083"],"abstract_inverted_index":{"We":[0,93],"study":[1],"the":[2,54,71,74,78,82,87,91,97,123,157,172,207,227,239,248],"integration":[3],"of":[4,56,90,125,131,164,209],"reinforcement":[5,101,261],"learning":[6,28,102,262,274],"and":[7,49,122,134,167,187,216,226,238,293],"spatial":[8,135,168,188,195,201,228,240,266],"knowledge":[9],"graph":[10],"for":[11],"incremental":[12,27,273],"mobile":[13,20,64],"user":[14,65,75,118,133,147,165,210],"profiling,":[15],"which":[16,170],"aims":[17],"to":[18,22,46,61,66,95,151,174,205,246,288,297],"map":[19],"users":[21,225,237],"dynamically-updated":[23],"profile":[24,76],"vectors":[25],"by":[26],"from":[29],"a":[30,41,63,100,108,114,117,128,132,146,153,161,200,233,260,279],"mixed-user":[31,280],"event":[32,144,281],"stream.":[33,282],"After":[34,180],"exploring":[35],"many":[36],"profiling":[37,51,277],"methods,":[38],"we":[39,190,219,258,284],"identify":[40,191,220],"new":[42,162],"imitation":[43],"based":[44,69],"criteria":[45],"better":[47],"evaluate":[48],"optimize":[50],"accuracy.":[52],"Considering":[53],"objective":[55],"teaching":[57],"an":[58,105,111,149],"autonomous":[59],"agent":[60,83,106,173],"imitate":[62],"plan":[67],"next-visit":[68,109],"on":[70],"user's":[72],"profile,":[73],"is":[77,107,113,127],"most":[79],"accurate":[80],"when":[81],"can":[84,271],"perfectly":[85],"mimic":[86],"activity":[88,139,291],"patterns":[89],"user.":[92],"propose":[94],"formulate":[96],"problem":[98],"into":[99,160],"task,":[103],"where":[104],"planner,":[110],"action":[112,150],"POI":[115],"that":[116,145,251],"will":[119,155],"visit":[120,152,177],"next,":[121],"state":[124,163,249],"environment":[126],"fused":[129],"representation":[130,250],"entities":[136],"(e.g.,":[137],"POIs,":[138],"types,":[140],"functional":[141],"zones).":[142],"An":[143],"takes":[148],"POI,":[154],"change":[156],"environment,":[158],"resulting":[159],"profiles":[166],"entities,":[169,189,196],"helps":[171],"predict":[175],"next":[176],"more":[178],"accurately.":[179],"analyzing":[181],"such":[182],"interactions":[183],"among":[184,194],"events,":[185],"users,":[186],"(1)semantic":[192],"connectivity":[193],"and,":[197,230],"thus,":[198,231],"introduce":[199],"Knowledge":[202],"Graph":[203],"(KG)":[204],"characterize":[206],"semantics":[208],"visits":[211],"over":[212,253],"connected":[213],"locations,":[214],"activities,":[215],"zones.":[217],"Besides,":[218],"(2)":[221],"mutual":[222],"influence":[223],"between":[224,236],"KG,":[229,241],"develop":[232,259],"mutual-updating":[234],"strategy":[235],"mixed":[242],"with":[243,265],"temporal":[244],"context,":[245],"quantify":[247],"evolves":[252],"time.":[254],"Along":[255],"these":[256],"lines,":[257],"framework":[263,270],"integrated":[264],"KG.":[267],"The":[268],"proposed":[269],"achieve":[272],"in":[275],"multi-user":[276],"given":[278],"Finally,":[283],"apply":[285],"our":[286],"approach":[287],"human":[289],"mobility":[290],"prediction":[292],"present":[294],"extensive":[295],"experiments":[296],"demonstrate":[298],"improved":[299],"performances.":[300]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}