{"id":"https://openalex.org/W7117657799","doi":"https://doi.org/10.3390/ijgi15010014","title":"Context-Aware Knowledge Graph Learning for Point-of-Interest Recommendation","display_name":"Context-Aware Knowledge Graph Learning for Point-of-Interest Recommendation","publication_year":2025,"publication_date":"2025-12-29","ids":{"openalex":"https://openalex.org/W7117657799","doi":"https://doi.org/10.3390/ijgi15010014"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi15010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010014","pdf_url":"https://www.mdpi.com/2220-9964/15/1/14/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/15/1/14/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yan Zhou","orcid":"https://orcid.org/0000-0003-3381-357X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I3018263800","display_name":"Huzhou University","ror":"https://ror.org/04mvpxy20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3018263800"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhou","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China"],"raw_orcid":"https://orcid.org/0000-0003-3381-357X","affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China","institution_ids":["https://openalex.org/I3018263800","https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121678561","display_name":"Di Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I3018263800","display_name":"Huzhou University","ror":"https://ror.org/04mvpxy20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3018263800"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Zhang","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China"],"raw_orcid":"https://orcid.org/0009-0003-1964-0614","affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China","institution_ids":["https://openalex.org/I3018263800","https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079364994","display_name":"Kaixuan Zhou","orcid":"https://orcid.org/0000-0002-0071-165X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixuan Zhou","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121639041","display_name":"Pengcheng Han","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengcheng Han","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.0267,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91787024,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"15","issue":"1","first_page":"14","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.397599995136261,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.397599995136261,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.3921000063419342,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.08550000190734863,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/knowledge-graph","display_name":"Knowledge graph","score":0.5737000107765198},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5701000094413757},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5184999704360962},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.42309999465942383},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.36329999566078186},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.35740000009536743},{"id":"https://openalex.org/keywords/semantic-memory","display_name":"Semantic memory","score":0.3529999852180481},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.3352000117301941},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.32409998774528503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459999918937683},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5701000094413757},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46399998664855957},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41269999742507935},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.3529999852180481},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3506999909877777},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30979999899864197},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C2777522414","wikidata":"https://www.wikidata.org/wiki/Q648457","display_name":"Social graph","level":3,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/ijgi15010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010014","pdf_url":"https://www.mdpi.com/2220-9964/15/1/14/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2108bce7c7e2411585094312656ddfed","is_oa":true,"landing_page_url":"https://doaj.org/article/2108bce7c7e2411585094312656ddfed","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":"ISPRS International Journal of Geo-Information, Vol 15, Iss 1, p 14 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/ijgi15010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15010014","pdf_url":"https://www.mdpi.com/2220-9964/15/1/14/pdf","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G438286652","display_name":null,"funder_award_id":"41871321","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4492079741","display_name":null,"funder_award_id":"42471465","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7792699363","display_name":null,"funder_award_id":"2022YFC3005702","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7117657799.pdf","grobid_xml":"https://content.openalex.org/works/W7117657799.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W1984189333","https://openalex.org/W2087692915","https://openalex.org/W2116341502","https://openalex.org/W2154851992","https://openalex.org/W2159094788","https://openalex.org/W2244405900","https://openalex.org/W2408538552","https://openalex.org/W2539781657","https://openalex.org/W2604314403","https://openalex.org/W2643577078","https://openalex.org/W2735318356","https://openalex.org/W2783944588","https://openalex.org/W2788872664","https://openalex.org/W2883204477","https://openalex.org/W2905432015","https://openalex.org/W2945623882","https://openalex.org/W2947144452","https://openalex.org/W2962756421","https://openalex.org/W3155936517","https://openalex.org/W3189845972","https://openalex.org/W3209428859","https://openalex.org/W4297963447","https://openalex.org/W4318812521","https://openalex.org/W4361275899","https://openalex.org/W4385614271","https://openalex.org/W4389805301","https://openalex.org/W4398174014","https://openalex.org/W4401626610","https://openalex.org/W4407407178","https://openalex.org/W4408441365","https://openalex.org/W4409244500","https://openalex.org/W4411358779"],"related_works":[],"abstract_inverted_index":{"Existing":[0],"point-of-interest":[1],"(POI)":[2],"recommendation":[3,177],"methods":[4],"often":[5],"fail":[6],"to":[7,104],"capture":[8],"complex":[9,182],"contextual":[10,173],"dependencies":[11,121],"and":[12,44,78,119,136,150,162,175,181],"suffer":[13],"from":[14],"severe":[15],"data":[16],"sparsity":[17],"in":[18,122,171],"location-based":[19],"social":[20,45,79],"networks":[21],"(LBSNs).":[22],"To":[23],"address":[24],"these":[25],"limitations,":[26],"this":[27],"study":[28],"proposes":[29],"a":[30,48,62,109],"Context-Aware":[31,55,83],"Knowledge":[32,56,84],"Graph":[33,57,85,112,129],"Learning":[34],"(CKGL)":[35],"method":[36],"that":[37,67,153],"integrates":[38],"multi-dimensional":[39],"semantic":[40],"information,":[41],"spatio-temporal":[42],"dependencies,":[43],"relationships":[46,70],"into":[47],"unified":[49,63],"knowledge":[50,65],"graph":[51,66],"framework.":[52],"First,":[53],"the":[54,82,91,127],"Construction":[58],"(CKGC)":[59],"module":[60],"builds":[61],"POI":[64],"captures":[68,116],"heterogeneous":[69,106,140],"among":[71],"users,":[72],"POIs,":[73],"regions":[74],"of":[75,102],"interest":[76],"(ROIs),":[77],"links.":[80],"Then,":[81],"Embedding":[86],"(CKGE)":[87],"module,":[88],"based":[89],"on":[90,144,160],"Translational":[92],"Distance":[93],"Model":[94],"with":[95],"Relation-Specific":[96],"Spaces":[97],"(TransR),":[98],"learns":[99],"relation-specific":[100],"embeddings":[101],"entities":[103],"preserve":[105],"semantics.":[107],"Next,":[108],"Spatio-Temporal":[110],"Gated":[111],"Neural":[113],"Network":[114,131],"(STG-GNN)":[115],"temporal":[117],"dynamics":[118],"spatial":[120],"user":[123],"check-in":[124],"behaviors,":[125],"while":[126],"Relation-Aware":[128],"Attention":[130],"(RA-GAT)":[132],"enhances":[133],"multi-relational":[134],"reasoning":[135],"information":[137],"aggregation":[138],"across":[139],"relations.":[141],"Extensive":[142],"experiments":[143],"two":[145],"real-world":[146],"LBSN":[147],"datasets,":[148],"Gowalla":[149],"Brightkite,":[151],"demonstrate":[152],"CKGL":[154],"significantly":[155],"outperforms":[156],"several":[157],"baseline":[158],"models":[159],"Recall":[161],"Normalized":[163],"Discounted":[164],"Cumulative":[165],"Gain":[166],"(NDCG),":[167],"validating":[168],"its":[169],"effectiveness":[170],"capturing":[172],"semantics":[174],"improving":[176],"accuracy":[178],"under":[179],"sparse":[180],"scenarios.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-12-31T00:00:00"}
