{"id":"https://openalex.org/W3003827402","doi":"https://doi.org/10.3233/faia200016","title":"Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model","display_name":"Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3003827402","doi":"https://doi.org/10.3233/faia200016","mag":"3003827402"},"language":"en","primary_location":{"id":"doi:10.3233/faia200016","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia200016","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5112568814","display_name":"Ry\u014dsuke Konishi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Konishi Ryosuke","raw_affiliation_strings":["Generic Solution Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Generic Solution Corporation","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100993314","display_name":"Fumito Nakamura","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nakamura Fumito","raw_affiliation_strings":["Generic Solution Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Generic Solution Corporation","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019351299","display_name":"Yasushi Kiyoki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kiyoki Yasushi","raw_affiliation_strings":["Faculty of Environment and Information Studies"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Environment and Information Studies","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5603,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65611782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.8007000088691711,"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"}},"topics":[{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.8007000088691711,"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"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.7720000147819519,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.7254999876022339,"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/logistic-regression","display_name":"Logistic regression","score":0.811803936958313},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7209023833274841},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6563502550125122},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6368825435638428},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4438122808933258},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.3664371967315674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23952233791351318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2318006455898285},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1927434504032135}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.811803936958313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7209023833274841},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6563502550125122},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6368825435638428},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4438122808933258},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3664371967315674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23952233791351318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2318006455898285},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1927434504032135}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia200016","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia200016","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W4391210591","https://openalex.org/W1582019636","https://openalex.org/W1499005795","https://openalex.org/W3172493050","https://openalex.org/W4385420271","https://openalex.org/W4312192618","https://openalex.org/W2593798266"],"abstract_inverted_index":{"Improved":[0],"computing":[1,83],"environments":[2],"performing":[3],"large-scale":[4],"data":[5,151,234,240],"processing":[6,10,229,237],"and":[7,36,78,95,130,167,173,188,233,236,257],"high-speed":[8],"computational":[9],"facilitate":[11],"the":[12,27,60,80,91,119,123,143,169,179,191,222,238,247,254],"delivery":[13],"of":[14,56,82,93,128,193,230,249],"new":[15],"algorithms":[16],"to":[17,48,53,70,89,115,134,142,148,205],"businesses":[18],"while":[19],"considering":[20],"cost":[21],"efficiency":[22],"for":[23,34,68,107,182],"small-scale":[24],"investments.":[25],"Implementing":[26],"proposed":[28,251],"method":[29,106,147,203,252],"more":[30,140,194],"as":[31,45,165],"a":[32,105,146,154,158,201],"criterion":[33],"feasibility":[35],"economic":[37],"rationality":[38],"in":[39,59,175,218,225,241],"specific":[40,141,204],"problem":[41],"areas":[42],"rather":[43],"than":[44],"an":[46],"approach":[47],"generic":[49],"issues,":[50],"we":[51],"aim":[52],"develop":[54],"technologies":[55],"practical":[57],"use":[58],"real":[61],"world.":[62],"Recently,":[63],"it":[64,85],"has":[65,86],"become":[66,87],"possible":[67,88],"customers":[69,207],"monitor":[71],"their":[72],"buying":[73,171],"behavior":[74],"through":[75,98,162,227],"smart":[76],"devices,":[77],"with":[79,126],"improvement":[81],"performance,":[84],"improve":[90],"accuracy":[92],"prediction":[94],"recommendation":[96,202],"cycles":[97],"active":[99],"online":[100,132],"learning.":[101],"This":[102],"study":[103],"proposes":[104],"dynamically":[108],"recommending":[109],"products":[110],"that":[111,138],"are":[112],"highly":[113],"likely":[114],"be":[116,216],"selected":[117],"by":[118,121,152,220],"user":[120],"combining":[122],"user's":[124,155,159],"reaction":[125],"reuse":[127],"knowledge":[129,223,231],"real-time":[131,219,242],"learning":[133,259],"cyclically":[135],"repeat":[136],"feedback":[137,187],"is":[139],"user.We":[144],"propose":[145,200],"sense":[149],"streaming":[150],"utilizing":[153],"behavior,":[156],"intervening":[157],"behavioral":[160],"change":[161],"interactions,":[163],"such":[164],"recommendations,":[166],"evaluating":[168],"user\u00e2\u0102\u0179s":[170],"intention":[172],"interest":[174],"each":[176],"product.":[177],"Using":[178],"evaluation":[180],"results":[181],"recommendations":[183],"helps":[184],"achieve":[185],"positive":[186],"effectively":[189],"support":[190],"selection":[192],"exciting":[195],"or":[196],"different":[197],"products.":[198],"We":[199,244],"individual":[206],"based":[208],"on":[209],"past":[210],"transaction":[211],"data,":[212],"where":[213],"changes":[214],"can":[215],"monitored":[217],"reusing":[221],"acquired":[224],"advance":[226],"batch":[228],"discovery":[232],"mining":[235],"stream":[239],"online.":[243],"will":[245],"present":[246],"implementation":[248],"our":[250],"targeting":[253],"database":[255],"system":[256],"machine":[258],"algorithm.":[260]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-02-07T00:00:00"}