{"id":"https://openalex.org/W2783438688","doi":"https://doi.org/10.1109/bigdata.2017.8258405","title":"Dynamic Bayesian predictive model for box office forecasting","display_name":"Dynamic Bayesian predictive model for box office forecasting","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783438688","doi":"https://doi.org/10.1109/bigdata.2017.8258405","mag":"2783438688"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258405","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5110116556","display_name":"Wutao Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wutao Wei","raw_affiliation_strings":["Microsoft, Redmond, WA, United States"],"affiliations":[{"raw_affiliation_string":"Microsoft, Redmond, WA, United States","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350631","display_name":"Le Zhang","orcid":"https://orcid.org/0000-0002-6930-8674"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le Zhang","raw_affiliation_strings":["Microsoft, Singapore"],"affiliations":[{"raw_affiliation_string":"Microsoft, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031955425","display_name":"Qi Ding","orcid":"https://orcid.org/0009-0002-3991-9957"},"institutions":[{"id":"https://openalex.org/I4210133485","display_name":"UnitedHealth Group (United States)","ror":"https://ror.org/04a8rt780","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133485"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qi Ding","raw_affiliation_strings":["UnitedHealth Group, Irvine, CA, United States"],"affiliations":[{"raw_affiliation_string":"UnitedHealth Group, Irvine, CA, United States","institution_ids":["https://openalex.org/I4210133485"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001190649","display_name":"Bingrou Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bingrou Zhou","raw_affiliation_strings":["Amazon, Seattle, WA, United States"],"affiliations":[{"raw_affiliation_string":"Amazon, Seattle, WA, United States","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110116556"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":0.1433,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54143229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3958","last_page":"3964"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11674","display_name":"Sports Analytics and Performance","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9718999862670898,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/box-office","display_name":"Box office","score":0.8311361074447632},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6776921153068542},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.612113356590271},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5990786552429199},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.593955934047699},{"id":"https://openalex.org/keywords/film-industry","display_name":"Film industry","score":0.5678081512451172},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5006754398345947},{"id":"https://openalex.org/keywords/competition","display_name":"Competition (biology)","score":0.4918479919433594},{"id":"https://openalex.org/keywords/realm","display_name":"Realm","score":0.47337764501571655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3792564868927002},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35393789410591125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3253723382949829},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.15177679061889648},{"id":"https://openalex.org/keywords/advertising","display_name":"Advertising","score":0.14612030982971191},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.14176759123802185},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12233468890190125}],"concepts":[{"id":"https://openalex.org/C2992750335","wikidata":"https://www.wikidata.org/wiki/Q877435","display_name":"Box office","level":2,"score":0.8311361074447632},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6776921153068542},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.612113356590271},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5990786552429199},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.593955934047699},{"id":"https://openalex.org/C54040653","wikidata":"https://www.wikidata.org/wiki/Q1415395","display_name":"Film industry","level":3,"score":0.5678081512451172},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5006754398345947},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.4918479919433594},{"id":"https://openalex.org/C2778757428","wikidata":"https://www.wikidata.org/wiki/Q1250464","display_name":"Realm","level":2,"score":0.47337764501571655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3792564868927002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35393789410591125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3253723382949829},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.15177679061889648},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.14612030982971191},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.14176759123802185},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12233468890190125},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C519580073","wikidata":"https://www.wikidata.org/wiki/Q41253","display_name":"Movie theater","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C52119013","wikidata":"https://www.wikidata.org/wiki/Q50637","display_name":"Art history","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258405","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W27156145","https://openalex.org/W186039566","https://openalex.org/W198123775","https://openalex.org/W281645550","https://openalex.org/W1587026990","https://openalex.org/W1969761972","https://openalex.org/W1972677429","https://openalex.org/W1973943669","https://openalex.org/W2008659066","https://openalex.org/W2012967345","https://openalex.org/W2015186536","https://openalex.org/W2049986915","https://openalex.org/W2058218386","https://openalex.org/W2062360407","https://openalex.org/W2074108366","https://openalex.org/W2095954269","https://openalex.org/W2150722745","https://openalex.org/W2169533279","https://openalex.org/W2295319989","https://openalex.org/W2741433478","https://openalex.org/W3104999193","https://openalex.org/W3125096521","https://openalex.org/W4229880569","https://openalex.org/W4230096730","https://openalex.org/W4231057675","https://openalex.org/W4239107213","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2278792348","https://openalex.org/W2159861630","https://openalex.org/W2383902699","https://openalex.org/W4210316726","https://openalex.org/W1992035678","https://openalex.org/W2218974371","https://openalex.org/W238210075","https://openalex.org/W3194131924","https://openalex.org/W4213124416","https://openalex.org/W2809631137"],"abstract_inverted_index":{"Film":[0],"industry":[1],"plays":[2],"a":[3,18,36,55,88,93,134],"vital":[4],"role":[5],"in":[6,10,26,51,84,100],"driving":[7],"economic":[8],"growth":[9],"the":[11,27,61,116,130],"modern":[12],"society.":[13],"Though":[14],"financial":[15],"gain":[16],"of":[17,35,63,129,137],"successful":[19],"movie":[20,37,43,71,138],"can":[21,45],"be":[22],"fabulously":[23],"huge,":[24],"competition":[25],"realm":[28],"is":[29],"intrinsically":[30],"competitive.":[31],"Box":[32],"office":[33,65,140],"forecating":[34],"therefore":[38],"becomes":[39],"significantly":[40],"pivotal":[41],"as":[42,69],"producers":[44],"properly":[46],"allocate":[47],"funds":[48],"and":[49,53,66,115],"resources":[50],"producing":[52],"distributing":[54],"movie.":[56],"Conventional":[57],"methods":[58],"statistically":[59],"model":[60,91,123],"correlation":[62],"box":[64,103,139],"indicators":[67],"such":[68],"classic":[70],"attributes":[72],"or":[73],"post-release":[74],"information":[75],"which":[76],"often":[77],"lack":[78],"real-time":[79],"efficiency.":[80],"The":[81,105],"proposed":[82,131],"method":[83,106,132],"this":[85],"paper":[86],"uses":[87],"dynamic":[89],"linear":[90],"with":[92],"Bayesian":[94],"framework,":[95],"for":[96,120],"an":[97],"improved":[98],"performance":[99],"predicting":[101],"daily":[102,117],"office.":[104],"considers":[107],"both":[108],"prior":[109],"knowledge":[110],"from":[111],"big":[112],"historical":[113],"data":[114,119,136],"refreshed":[118],"dynamically":[121],"updating":[122],"coefficients.":[124],"Experimental":[125],"results":[126],"prove":[127],"efficacy":[128],"on":[133],"sample":[135],"revenue.":[141]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
