{"id":"https://openalex.org/W2032899807","doi":"https://doi.org/10.1109/icnc.2014.6975935","title":"Context-aware smartphone application category recommender system with modularized Bayesian networks","display_name":"Context-aware smartphone application category recommender system with modularized Bayesian networks","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W2032899807","doi":"https://doi.org/10.1109/icnc.2014.6975935","mag":"2032899807"},"language":"en","primary_location":{"id":"doi:10.1109/icnc.2014.6975935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2014.6975935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 10th International Conference on Natural Computation (ICNC)","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/A5005851249","display_name":"Woo-Hyun Rho","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Woo-Hyun Rho","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul, Korea","Department of Computer Science,Yonsei University, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Department of Computer Science,Yonsei University, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102775641","display_name":"Sung-Bae Cho","orcid":"https://orcid.org/0000-0002-0185-1769"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung-Bae Cho","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul, Korea","Department of Computer Science,Yonsei University, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Department of Computer Science,Yonsei University, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":null,"first_page":"775","last_page":"779"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9983999729156494,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9923999905586243,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9878000020980835,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8663644194602966},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.8503303527832031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8231390714645386},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7462384700775146},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.7315648794174194},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7057951092720032},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6010789275169373},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5745173692703247},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5251628756523132},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4731539189815521},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.44549161195755005},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.429251104593277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4035177528858185},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.37701234221458435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07594627141952515},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06765839457511902}],"concepts":[{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8663644194602966},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.8503303527832031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8231390714645386},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7462384700775146},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.7315648794174194},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7057951092720032},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6010789275169373},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5745173692703247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5251628756523132},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4731539189815521},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44549161195755005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.429251104593277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4035177528858185},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.37701234221458435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07594627141952515},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06765839457511902},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc.2014.6975935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2014.6975935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 10th International Conference on Natural Computation (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","score":0.5600000023841858,"display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1499196531","https://openalex.org/W1896930264","https://openalex.org/W2023525859","https://openalex.org/W2025723605","https://openalex.org/W2046586564","https://openalex.org/W2048076161","https://openalex.org/W2049920566","https://openalex.org/W2056102005","https://openalex.org/W2069870183","https://openalex.org/W2096235681","https://openalex.org/W2104239624","https://openalex.org/W2110190160","https://openalex.org/W2113176580","https://openalex.org/W2150886314","https://openalex.org/W2158618310","https://openalex.org/W2306005907","https://openalex.org/W2396214858","https://openalex.org/W6698187099","https://openalex.org/W6712002563"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W2735929803","https://openalex.org/W4220714703","https://openalex.org/W1484355083","https://openalex.org/W3008845055","https://openalex.org/W2098758514","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622","https://openalex.org/W2556532874"],"abstract_inverted_index":{"The":[0],"number":[1,11],"of":[2,12,62,66,88,122,137],"applications":[3,20],"available":[4],"since":[5],"the":[6,10,34,60,67,79,111,120],"late":[7],"2010's,":[8],"and":[9,109,116],"smartphone":[13,98],"user":[14],"sharply":[15],"increasing.":[16],"However,":[17],"not":[18,22],"all":[19],"are":[21],"useful":[23],"or":[24,85],"helpful.":[25],"In":[26,92],"other":[27],"words,":[28],"to":[29,41,46,106],"obtain":[30],"satisfactory":[31],"results":[32],"in":[33],"search":[35,47],"can":[36],"be":[37],"difficult":[38],"means.":[39],"Users":[40],"find":[42],"what":[43],"they":[44],"want":[45],"for":[48],"a":[49,97],"many":[50],"times.":[51],"To":[52],"solve":[53],"this":[54,93],"problem,":[55],"previous":[56],"studies":[57],"have":[58,118],"proposed":[59,131],"use":[61,104],"recommender":[63],"systems.":[64],"Most":[65],"system":[68,132],"uses":[69],"age,":[70],"gender,":[71],"preference":[72],"based":[73],"collaborative":[74],"filtering.":[75],"Collaborative":[76],"filtering":[77],"has":[78],"problem":[80],"that":[81],"data":[82],"sparsity,":[83],"cold-start":[84],"needs":[86],"lots":[87],"users'":[89],"personal":[90],"data.":[91,127],"paper,":[94],"we":[95,117],"propose":[96],"context-aware":[99],"application":[100],"category":[101,112,124],"recommendation.":[102],"We":[103,128],"Bayesian-network":[105],"inference":[107,114,138],"context":[108,115],"recommend":[110],"when":[113],"set":[119],"probability":[121],"using":[123],"from":[125],"collected":[126],"tested":[129],"our":[130],"with":[133],"F1":[134],"measure,":[135],"accuracy":[136],"context.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}