{"id":"https://openalex.org/W4403582527","doi":"https://doi.org/10.1145/3627673.3679893","title":"CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching","display_name":"CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582527","doi":"https://doi.org/10.1145/3627673.3679893"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679893","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/A5045837039","display_name":"Shuai Xu","orcid":"https://orcid.org/0000-0002-5734-3616"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Xu","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5734-3616","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003852858","display_name":"Donghai Guan","orcid":"https://orcid.org/0000-0002-8448-9020"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghai Guan","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-8448-9020","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4148","last_page":"4152"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9904000163078308,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9605000019073486,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.716605544090271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6432806849479675},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5672512650489807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4337226152420044},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35130345821380615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16210129857063293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.118956059217453}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.716605544090271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6432806849479675},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5672512650489807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4337226152420044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35130345821380615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16210129857063293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.118956059217453},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679893","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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":30,"referenced_works":["https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2444485119","https://openalex.org/W2534727297","https://openalex.org/W2794156907","https://openalex.org/W2889535606","https://openalex.org/W2911778742","https://openalex.org/W2913696439","https://openalex.org/W2930399901","https://openalex.org/W2945431268","https://openalex.org/W2962687275","https://openalex.org/W2965539152","https://openalex.org/W2966783050","https://openalex.org/W3007674884","https://openalex.org/W3032521456","https://openalex.org/W3045509867","https://openalex.org/W3104097132","https://openalex.org/W3150739942","https://openalex.org/W3165667847","https://openalex.org/W3168146412","https://openalex.org/W3176170278","https://openalex.org/W4283379060","https://openalex.org/W4284696428","https://openalex.org/W4294170691","https://openalex.org/W4365795631","https://openalex.org/W4381379021","https://openalex.org/W4384655702","https://openalex.org/W4384659442","https://openalex.org/W4384774596","https://openalex.org/W4396794084"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Current":[0],"studies":[1],"mainly":[2],"rely":[3],"on":[4,173],"overlapping":[5,30],"users":[6,31],"(who":[7],"leave":[8],"trajectories":[9],"in":[10,20,64,73,81,101,139],"both":[11,57,140],"cities)":[12],"as":[13,114,116,128],"a":[14,39,88,129],"medium":[15],"to":[16,28,50,70,93,151],"learn":[17],"travelers'":[18],"preference":[19,138],"the":[21,51,61,65,74,95,102,153,161,177,182],"target":[22,75,103,143],"city,":[23],"however":[24],"it":[25],"is":[26,149],"unrealistic":[27],"find":[29],"when":[32],"two":[33],"cities":[34,158],"are":[35,119],"far":[36],"apart,":[37],"thus":[38],"severe":[40],"data":[41],"scarcity":[42],"issue":[43],"exists":[44],"for":[45,121,132],"this":[46,82],"problem.":[47],"Besides,":[48],"due":[49],"mixture":[52],"of":[53,98,181],"mobility":[54,97,137],"pattern":[55,118],"from":[56],"cities,":[58],"directly":[59],"applying":[60],"model":[62],"trained":[63],"source":[66,141],"city":[67],"may":[68],"lead":[69],"negative":[71],"transfer":[72],"city.":[76,104],"To":[77],"tackle":[78],"these":[79],"issues,":[80],"paper,":[83],"we":[84],"conceive":[85],"and":[86,142,159,179],"implement":[87],"novel":[89],"framework":[90],"called":[91],"CrossPred":[92],"predict":[94],"cross-city":[96,122,167],"long-distance":[99],"travelers":[100],"Specifically,":[105],"POI":[106,123,155,163,168],"features":[107,156],"including":[108],"popularity,":[109],"textual":[110],"description,":[111],"spatial":[112],"distribution":[113],"well":[115],"sequential":[117],"considered":[120],"matching,":[124],"which":[125],"further":[126],"acts":[127],"vital":[130],"link":[131],"jointly":[133],"modeling":[134],"native":[135],"user":[136],"cities.":[144],"Maximum":[145],"Mean":[146],"Discrepancy":[147],"(MMD)":[148],"adopted":[150],"strengthen":[152],"shared":[154],"among":[157],"weaken":[160],"unique":[162],"features,":[164],"thereby":[165],"promoting":[166],"feature":[169],"matching.":[170],"Extensive":[171],"experiments":[172],"real-world":[174],"datasets":[175],"demonstrate":[176],"effectiveness":[178],"superiority":[180],"proposed":[183],"framework.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
