{"id":"https://openalex.org/W4387846649","doi":"https://doi.org/10.1145/3583780.3615194","title":"Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding","display_name":"Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846649","doi":"https://doi.org/10.1145/3583780.3615194"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615194","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-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/A5101476767","display_name":"W. H. Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I55022517","display_name":"Heilongjiang University","ror":"https://ror.org/04zyhq975","country_code":"CN","type":"education","lineage":["https://openalex.org/I55022517"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiliang Chan","raw_affiliation_strings":["Heilongjiang University, Harbin Shi, China"],"affiliations":[{"raw_affiliation_string":"Heilongjiang University, Harbin Shi, China","institution_ids":["https://openalex.org/I55022517"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101523305","display_name":"Qianqian Ren","orcid":"https://orcid.org/0000-0003-1171-7018"},"institutions":[{"id":"https://openalex.org/I55022517","display_name":"Heilongjiang University","ror":"https://ror.org/04zyhq975","country_code":"CN","type":"education","lineage":["https://openalex.org/I55022517"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianqian Ren","raw_affiliation_strings":["Heilongjiang University, Harbin Shi, China"],"affiliations":[{"raw_affiliation_string":"Heilongjiang University, Harbin Shi, China","institution_ids":["https://openalex.org/I55022517"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101476767"],"corresponding_institution_ids":["https://openalex.org/I55022517"],"apc_list":null,"apc_paid":null,"fwci":2.7618,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.89804839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3763","last_page":"3767"},"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":0.9994999766349792,"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":0.9994999766349792,"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":0.9894999861717224,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9854999780654907,"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":0.7250795960426331},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6143171787261963},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5709460973739624},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5194110870361328},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5041378736495972},{"id":"https://openalex.org/keywords/neighbourhood","display_name":"Neighbourhood (mathematics)","score":0.48352718353271484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48195549845695496},{"id":"https://openalex.org/keywords/pointer","display_name":"Pointer (user interface)","score":0.4658217132091522},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.45035654306411743},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44303110241889954},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4416007995605469},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1391083002090454},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09075561165809631}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250795960426331},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6143171787261963},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5709460973739624},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5194110870361328},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5041378736495972},{"id":"https://openalex.org/C161677786","wikidata":"https://www.wikidata.org/wiki/Q2478475","display_name":"Neighbourhood (mathematics)","level":2,"score":0.48352718353271484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48195549845695496},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.4658217132091522},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.45035654306411743},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44303110241889954},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4416007995605469},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1391083002090454},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09075561165809631},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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:10.1145/3583780.3615194","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615194","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7681496740","display_name":null,"funder_award_id":"2022M711088","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W631319203","https://openalex.org/W1888005072","https://openalex.org/W2135046866","https://openalex.org/W2604230684","https://openalex.org/W2768009948","https://openalex.org/W2807954821","https://openalex.org/W2903883820","https://openalex.org/W2904403013","https://openalex.org/W2962756421","https://openalex.org/W3032901728","https://openalex.org/W3034277777","https://openalex.org/W3035580605","https://openalex.org/W3093894742","https://openalex.org/W3213025081","https://openalex.org/W4236908777","https://openalex.org/W4288072853","https://openalex.org/W4306317901"],"related_works":["https://openalex.org/W4393363920","https://openalex.org/W4248744973","https://openalex.org/W2081900870","https://openalex.org/W4383700488","https://openalex.org/W4287780535","https://openalex.org/W3037015124","https://openalex.org/W3021699323","https://openalex.org/W1969064169","https://openalex.org/W2129100545","https://openalex.org/W4390871823"],"abstract_inverted_index":{"Urban":[0],"region":[1,51,59],"embedding":[2],"is":[3,109],"an":[4],"important":[5],"and":[6,15,38,77,99],"yet":[7],"highly":[8],"challenging":[9],"issue":[10],"due":[11],"to":[12,34,87,112,118,140],"the":[13,24,46,68],"complexity":[14],"constantly":[16],"changing":[17],"nature":[18],"of":[19,42,48,90,102],"urban":[20,43,58,63],"data.":[21,64],"To":[22,96],"address":[23],"challenges,":[25],"we":[26,66,81],"propose":[27],"a":[28,105],"Region-Wise":[29],"Multi-View":[30],"Representation":[31],"Learning":[32],"(ROMER)":[33],"capture":[35,67],"multi-view":[36,69,120],"dependencies":[37],"learn":[39,57,88,113],"expressive":[40],"representations":[41],"regions":[44],"without":[45],"constraints":[47],"rigid":[49],"neighbourhood":[50],"conditions.":[52],"Our":[53],"model":[54,134],"focuses":[55],"on":[56,128],"representation":[60],"from":[61,71],"multi-source":[62],"First,":[65],"correlations":[70],"mobility":[72],"flow":[73],"patterns,":[74],"POI":[75],"semantics":[76],"check-in":[78],"dynamics.":[79],"Then,":[80],"adopt":[82],"global":[83],"graph":[84],"attention":[85,117],"networks":[86],"similarity":[89],"any":[91],"two":[92,125],"vertices":[93],"in":[94],"graphs.":[95],"comprehensively":[97],"consider":[98],"share":[100],"features":[101],"multiple":[103],"views,":[104],"two-stage":[106],"fusion":[107],"module":[108],"further":[110],"proposed":[111],"weights":[114],"with":[115],"external":[116],"fuse":[119],"embeddings.":[121],"Extensive":[122],"experiments":[123],"for":[124],"downstream":[126],"tasks":[127],"real-world":[129],"datasets":[130],"demonstrate":[131],"that":[132],"our":[133],"outperforms":[135],"state-of-the-art":[136],"methods":[137],"by":[138],"up":[139],"17%":[141],"improvement.":[142]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
