{"id":"https://openalex.org/W4406894925","doi":"https://doi.org/10.1109/ictai62512.2024.00054","title":"Where to go Next ? Social and Spatio-Temporal Learning for Next Points-of-Interest Prediction Using Residual Vision Transformer","display_name":"Where to go Next ? Social and Spatio-Temporal Learning for Next Points-of-Interest Prediction Using Residual Vision Transformer","publication_year":2024,"publication_date":"2024-10-28","ids":{"openalex":"https://openalex.org/W4406894925","doi":"https://doi.org/10.1109/ictai62512.2024.00054"},"language":"en","primary_location":{"id":"doi:10.1109/ictai62512.2024.00054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictai62512.2024.00054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)","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/A5029222217","display_name":"Sayda Elmi","orcid":"https://orcid.org/0000-0002-9091-2307"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]},{"id":"https://openalex.org/I40253897","display_name":"University of New Haven","ror":"https://ror.org/00zm4rq24","country_code":"US","type":"education","lineage":["https://openalex.org/I40253897"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sayda Elmi","raw_affiliation_strings":["University of New Haven, Yale University,New Haven,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Haven, Yale University,New Haven,USA","institution_ids":["https://openalex.org/I40253897","https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5092655082","display_name":"Sai Karthik Navuluru","orcid":null},"institutions":[{"id":"https://openalex.org/I40253897","display_name":"University of New Haven","ror":"https://ror.org/00zm4rq24","country_code":"US","type":"education","lineage":["https://openalex.org/I40253897"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Karthik Navuluru","raw_affiliation_strings":["University of New Haven,New Haven,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Haven,New Haven,USA","institution_ids":["https://openalex.org/I40253897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24611468,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"319","last_page":"326"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9922000169754028,"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"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9922000169754028,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.955299973487854,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9228000044822693,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7352931499481201},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.603181779384613},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5314107537269592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5115064382553101},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4316812753677368},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33600544929504395},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15806794166564941},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1064915657043457},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09222716093063354},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07370314002037048}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7352931499481201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.603181779384613},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5314107537269592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5115064382553101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4316812753677368},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33600544929504395},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15806794166564941},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1064915657043457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09222716093063354},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07370314002037048}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictai62512.2024.00054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictai62512.2024.00054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W177101540","https://openalex.org/W2073013176","https://openalex.org/W2087692915","https://openalex.org/W2110953678","https://openalex.org/W2539781657","https://openalex.org/W2739805805","https://openalex.org/W2767584206","https://openalex.org/W2798385737","https://openalex.org/W2905432015","https://openalex.org/W3012782947","https://openalex.org/W3035308866","https://openalex.org/W3155157989","https://openalex.org/W4226506339","https://openalex.org/W4390030007","https://openalex.org/W6632885370"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"Recommendation":[0],"engines":[1],"based":[2,43],"on":[3,44,133],"location":[4],"help":[5],"users":[6],"find":[7],"visually":[8],"appealing":[9],"Points-of-Interest":[10],"(POI).":[11],"Next":[12,35],"POIs":[13,29],"recommendation":[14],"is":[15,66,75,115],"very":[16],"significant":[17,124],"and":[18,48,60,71,139,153],"advantageous.":[19],"In":[20],"this":[21,148],"paper,":[22],"we":[23],"propose":[24],"a":[25,38,72],"network":[26,47],"for":[27],"next":[28],"prediction,":[30],"called":[31],"\u201cWhere":[32],"to":[33,78,98],"Go":[34],"(WTG)\u201d,":[36],"employing":[37],"hybrid":[39],"deep":[40],"learning":[41],"model":[42],"residual":[45],"neural":[46],"vision":[49,109],"trans-former.":[50],"WTG":[51],"method":[52],"models":[53],"multiple":[54],"features,":[55],"including":[56],"spatio-temporal,":[57],"sequential,":[58],"social,":[59],"context":[61,112],"features.":[62],"Users'":[63],"mobility":[64],"behavior":[65],"first":[67],"modeled":[68],"as":[69],"images":[70],"new":[73],"architecture":[74],"then":[76,104],"used":[77],"capture":[79],"the":[80,87,100,108,118,145,156,160],"spatio-temporal":[81],"patterns":[82],"while":[83],"taking":[84],"into":[85,117],"account":[86],"social":[88],"relations":[89],"connecting":[90],"users.":[91],"The":[92,141],"sequential":[93],"features":[94],"that":[95],"are":[96,103],"necessary":[97],"characterize":[99],"user":[101],"behaviors":[102],"extracted":[105],"using":[106,155],"an":[107],"transformer.":[110],"Moreover,":[111],"feature":[113],"extraction":[114],"integrated":[116],"model.":[119],"We":[120],"demonstrate":[121],"our":[122],"model's":[123],"superiority":[125],"over":[126],"eight":[127],"existing":[128],"methods":[129],"by":[130],"evaluating":[131],"it":[132],"two":[134],"real":[135],"location-based":[136],"networks:":[137],"Foursquare":[138],"Gowalla.":[140],"source":[142],"code":[143],"supporting":[144],"findings":[146],"of":[147],"study":[149],"can":[150],"be":[151],"accessed":[152],"reproduced":[154],"materials":[157],"provided":[158],"at":[159],"following":[161],"repository:":[162],"https://anonymous.4open.science/r/WTG-Next-ASCC.":[163]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}