{"id":"https://openalex.org/W4399120428","doi":"https://doi.org/10.1145/3659211.3659341","title":"Analysis and Prediction of Tourism Destination Popularity Based on Data Mining","display_name":"Analysis and Prediction of Tourism Destination Popularity Based on Data Mining","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4399120428","doi":"https://doi.org/10.1145/3659211.3659341"},"language":"en","primary_location":{"id":"doi:10.1145/3659211.3659341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Big Data Economy and Information 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":null,"display_name":"Shufen Liang","orcid":"https://orcid.org/0009-0007-1744-1899"},"institutions":[{"id":"https://openalex.org/I2800655693","display_name":"Lyceum of the Philippines University","ror":"https://ror.org/027e56k73","country_code":"PH","type":"education","lineage":["https://openalex.org/I2800655693"]}],"countries":["PH"],"is_corresponding":true,"raw_author_name":"Shufen Liang","raw_affiliation_strings":["Lyceum of the Philippines University, Philippines and \rZhengzhou shengda university, China"],"raw_orcid":"https://orcid.org/0009-0007-1744-1899","affiliations":[{"raw_affiliation_string":"Lyceum of the Philippines University, Philippines and \rZhengzhou shengda university, China","institution_ids":["https://openalex.org/I2800655693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2800655693"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39264866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"756","last_page":"760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T10609","display_name":"Digital Marketing and Social Media","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T11474","display_name":"Sport and Mega-Event Impacts","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T10055","display_name":"Diverse Aspects of Tourism Research","score":0.9569000005722046,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/popularity","display_name":"Popularity","score":0.8809888958930969},{"id":"https://openalex.org/keywords/tourism","display_name":"Tourism","score":0.821381688117981},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7434931993484497},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6921266317367554},{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.6523690223693848},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5389270782470703},{"id":"https://openalex.org/keywords/destinations","display_name":"Destinations","score":0.46052441000938416},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4213883578777313},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4180991053581238},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4164009988307953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3244119882583618},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1481761932373047}],"concepts":[{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.8809888958930969},{"id":"https://openalex.org/C18918823","wikidata":"https://www.wikidata.org/wiki/Q49389","display_name":"Tourism","level":2,"score":0.821381688117981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7434931993484497},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6921266317367554},{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.6523690223693848},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5389270782470703},{"id":"https://openalex.org/C2776687071","wikidata":"https://www.wikidata.org/wiki/Q5265193","display_name":"Destinations","level":3,"score":0.46052441000938416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4213883578777313},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4180991053581238},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4164009988307953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3244119882583618},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1481761932373047},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3659211.3659341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2897357365","https://openalex.org/W3198482711","https://openalex.org/W4312673786","https://openalex.org/W4313333069","https://openalex.org/W4317192427"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2170801710","https://openalex.org/W3191198889","https://openalex.org/W4399767560"],"abstract_inverted_index":{"The":[0],"proliferation":[1],"of":[2,24,29,60,136],"data":[3,15,50],"generated":[4],"by":[5],"online":[6],"users":[7],"presents":[8],"a":[9,52,57],"significant":[10],"opportunity":[11],"for":[12,20,32,118,147],"analyzing":[13],"this":[14,131],"and":[16,46,77,84,103,144],"developing":[17],"automated":[18],"methods":[19],"predicting":[21],"the":[22,35,93,105,109,134,141],"popularity":[23,79],"tourism":[25,54,61,120,142],"destinations.":[26,62],"This":[27,43,112],"is":[28],"great":[30],"importance":[31],"businesses":[33],"in":[34,101,140,150],"travel":[36],"industry":[37,143],"to":[38,128],"make":[39],"informed":[40],"operational":[41],"decisions.":[42],"study":[44],"integrates":[45],"cleanses":[47],"user":[48],"review":[49],"from":[51],"large":[53],"website,":[55],"creating":[56],"text-rich":[58],"dataset":[59],"Text":[63],"features":[64,102],"are":[65,87],"extracted":[66],"based":[67,123],"on":[68,108,124],"word":[69],"frequency":[70],"analysis,":[71],"combined":[72],"with":[73],"time":[74],"series":[75],"features,":[76],"two":[78],"prediction":[80,122],"models,":[81],"linear":[82,106],"regression":[83],"random":[85,94],"forests,":[86],"built.":[88],"Experimental":[89],"results":[90],"show":[91],"that":[92],"forest":[95],"model":[96,107],"efficiently":[97],"captures":[98],"nonlinear":[99],"relationships":[100],"outperforms":[104],"test":[110],"set.":[111],"approach":[113],"provides":[114,145],"an":[115],"effective":[116],"example":[117],"data-driven":[119],"hotspot":[121,148],"user-generated":[125],"content.":[126],"Compared":[127],"traditional":[129],"methods,":[130],"research":[132],"demonstrates":[133],"potential":[135],"machine":[137],"learning":[138],"techniques":[139],"insights":[146],"analysis":[149],"other":[151],"domains.":[152]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}