{"id":"https://openalex.org/W2538361144","doi":"https://doi.org/10.1145/2983323.2983655","title":"A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection","display_name":"A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection","publication_year":2016,"publication_date":"2016-10-24","ids":{"openalex":"https://openalex.org/W2538361144","doi":"https://doi.org/10.1145/2983323.2983655","mag":"2538361144"},"language":"en","primary_location":{"id":"doi:10.1145/2983323.2983655","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on 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/A5100781251","display_name":"Jin Li","orcid":"https://orcid.org/0000-0002-1422-8853"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Jin","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068518741","display_name":"Zhuonan Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuonan Feng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101756436","display_name":"Ling Feng","orcid":"https://orcid.org/0000-0001-7378-4342"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Feng","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100781251"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.8295,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85746433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2137","last_page":"2142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9980000257492065,"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/T12303","display_name":"Tensor decomposition and applications","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9889000058174133,"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/tensor-decomposition","display_name":"Tensor decomposition","score":0.6961991786956787},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6466103792190552},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6193943619728088},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5977571606636047},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5550501942634583},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.550764262676239},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4944446086883545},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.48196741938591003},{"id":"https://openalex.org/keywords/urban-computing","display_name":"Urban computing","score":0.4458768367767334},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43900877237319946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34796327352523804},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2768779993057251},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.22858473658561707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1113082766532898}],"concepts":[{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6961991786956787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466103792190552},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6193943619728088},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5977571606636047},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5550501942634583},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.550764262676239},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4944446086883545},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.48196741938591003},{"id":"https://openalex.org/C2778459138","wikidata":"https://www.wikidata.org/wiki/Q7900107","display_name":"Urban computing","level":2,"score":0.4458768367767334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43900877237319946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34796327352523804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2768779993057251},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.22858473658561707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1113082766532898},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2983323.2983655","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W98895690","https://openalex.org/W1586775974","https://openalex.org/W1913879219","https://openalex.org/W1999110238","https://openalex.org/W1999432478","https://openalex.org/W2024165284","https://openalex.org/W2034082601","https://openalex.org/W2070781416","https://openalex.org/W2089036804","https://openalex.org/W2093855404","https://openalex.org/W2094283130","https://openalex.org/W2102937240","https://openalex.org/W2112738128","https://openalex.org/W2117618130","https://openalex.org/W2130104690","https://openalex.org/W2136975357","https://openalex.org/W2294628100","https://openalex.org/W2294723619","https://openalex.org/W6604031572"],"related_works":["https://openalex.org/W4379256054","https://openalex.org/W2093953080","https://openalex.org/W2911706637","https://openalex.org/W47805180","https://openalex.org/W2963838862","https://openalex.org/W3015641590","https://openalex.org/W3216281372","https://openalex.org/W2987657992","https://openalex.org/W2949531434","https://openalex.org/W4286927328"],"abstract_inverted_index":{"Urban":[0],"black":[1,57,137],"hole,":[2],"as":[3],"a":[4,72,88,149],"traffic":[5],"anomaly,":[6],"has":[7],"caused":[8],"lots":[9],"of":[10,38,62,84],"catastrophic":[11],"accidents":[12],"in":[13,59],"many":[14],"big":[15],"cities":[16],"nowadays.":[17],"Traditional":[18],"methods":[19],"only":[20],"depend":[21],"on":[22],"the":[23,46,55,81,85,95,109,135,155],"single":[24,150],"source":[25],"data":[26,78],"(e.g.,":[27],"taxi":[28],"trajectories)":[29],"to":[30,44,107,132],"design":[31],"blackhole":[32,110],"detection":[33],"algorithm":[34],"from":[35,97],"one":[36],"point":[37],"view,":[39],"which":[40],"is":[41],"rather":[42],"incomplete":[43],"describe":[45],"regional":[47],"crowd":[48],"flow.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,93],"model":[54,127],"urban":[56,136],"holes":[58,138],"each":[60],"region":[61],"New":[63],"York":[64],"City":[65],"(NYC)":[66],"at":[67],"different":[68],"time":[69],"intervals":[70],"with":[71,128],"3-dimensional":[73],"tensor":[74,86,90],"by":[75,148],"fusing":[76],"cross-domain":[77],"sources.":[79],"Supplementing":[80],"missing":[82],"entries":[83],"through":[87],"context-aware":[89],"decomposition":[91],"approach,":[92],"leverage":[94],"knowledge":[96],"geographical":[98],"features,":[99],"311":[100],"complaint":[101],"features":[102,106],"and":[103,120],"human":[104],"mobility":[105],"recover":[108],"situation":[111],"throughout":[112],"NYC.":[113],"The":[114],"information":[115],"can":[116],"facilitate":[117],"local":[118],"residents":[119],"officials'":[121],"decision":[122],"making.":[123],"We":[124],"evaluate":[125],"our":[126],"five":[129],"datasets":[130],"related":[131],"NYC,":[133],"diagnosing":[134],"that":[139],"cannot":[140],"be":[141],"identified":[142],"(or":[143],"earlier":[144],"than":[145],"those":[146],"detected)":[147],"dataset.":[151],"Experimental":[152],"results":[153],"demonstrate":[154],"advantages":[156],"beyond":[157],"four":[158],"baseline":[159],"methods.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}