{"id":"https://openalex.org/W3136912207","doi":"https://doi.org/10.1109/bigdata50022.2020.9378439","title":"Real-time Karaoke Recommendations : Session-based Multi-Task Recommendations with Multivariate RNNs","display_name":"Real-time Karaoke Recommendations : Session-based Multi-Task Recommendations with Multivariate RNNs","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136912207","doi":"https://doi.org/10.1109/bigdata50022.2020.9378439","mag":"3136912207"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5109501933","display_name":"Shigeki Tanaka","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Shigeki Tanaka","raw_affiliation_strings":["Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015984301","display_name":"Ryoki Wakamoto","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryoki Wakamoto","raw_affiliation_strings":["Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029670252","display_name":"Yusuke Fukazawa","orcid":"https://orcid.org/0000-0001-9834-9339"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yusuke Fukazawa","raw_affiliation_strings":["Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Service Innovation Dept., NTT DOCOMO, INC, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109501933"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.33714933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1402","last_page":"1409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9986000061035156,"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":0.9986000061035156,"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/T11309","display_name":"Music and Audio Processing","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9739999771118164,"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/session","display_name":"Session (web analytics)","score":0.8099265098571777},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7522028088569641},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7502037286758423},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.7174797058105469},{"id":"https://openalex.org/keywords/mood","display_name":"Mood","score":0.6232126951217651},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6119231581687927},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.48758143186569214},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4831078052520752},{"id":"https://openalex.org/keywords/singing","display_name":"Singing","score":0.43917131423950195},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.3732433617115021},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36905744671821594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34353527426719666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31768709421157837},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3030547797679901},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.23945128917694092},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1891184151172638}],"concepts":[{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.8099265098571777},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7522028088569641},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7502037286758423},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.7174797058105469},{"id":"https://openalex.org/C2780733359","wikidata":"https://www.wikidata.org/wiki/Q331769","display_name":"Mood","level":2,"score":0.6232126951217651},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6119231581687927},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.48758143186569214},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4831078052520752},{"id":"https://openalex.org/C44819458","wikidata":"https://www.wikidata.org/wiki/Q27939","display_name":"Singing","level":2,"score":0.43917131423950195},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.3732433617115021},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36905744671821594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34353527426719666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31768709421157837},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3030547797679901},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.23945128917694092},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1891184151172638},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1921081011","https://openalex.org/W2011805610","https://openalex.org/W2041022395","https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2137028279","https://openalex.org/W2147654806","https://openalex.org/W2156387975","https://openalex.org/W2159094788","https://openalex.org/W2172140247","https://openalex.org/W2512965516","https://openalex.org/W2610932492","https://openalex.org/W2625746539","https://openalex.org/W2766736793","https://openalex.org/W2964121744","https://openalex.org/W2964199361","https://openalex.org/W2964316331","https://openalex.org/W2965683718","https://openalex.org/W3098231197","https://openalex.org/W3101588560","https://openalex.org/W3102201080","https://openalex.org/W4253760012","https://openalex.org/W4299286960","https://openalex.org/W6631190155","https://openalex.org/W6682889407","https://openalex.org/W6692935382"],"related_works":["https://openalex.org/W2390529913","https://openalex.org/W2142368101","https://openalex.org/W2372249404","https://openalex.org/W2367547137","https://openalex.org/W2354994102","https://openalex.org/W2387733758","https://openalex.org/W2376664795","https://openalex.org/W2366077683","https://openalex.org/W1501596003","https://openalex.org/W2368036937"],"abstract_inverted_index":{"There":[0],"are":[1,51,74,119],"more":[2,37,312],"than":[3],"2,000":[4],"Karaoke":[5,61,77,109,271],"stores":[6],"in":[7,32,45,63,124,233,281,300],"Japan":[8],"and":[9,39,57,79,112,139,147,174,200,210,219,252,304],"approximately":[10],"50":[11],"million":[12],"users":[13],"visit":[14,111],"every":[15],"year":[16],"for":[17,107,162,191,230,269],"a":[18,29,96,108,234,249,263,301],"singing":[19],"experience.":[20],"For":[21],"Karaoke,":[22],"recommendations":[23],"of":[24,43,59,85,166,188,194,248,298],"the":[25,41,55,60,76,80,125,136,145,159,167,175,186,189,213,228,241,246,258,278],"next":[26,168,176],"songs":[27,38,68,146,314],"play":[28],"significant":[30],"role":[31],"leading":[33],"customers":[34],"to":[35,54,103,121,149,155,243,253,287,291,294,310],"explore":[36],"make":[40],"most":[42],"time":[44,153,303],"Karaoke.":[46],"However,":[47],"few":[48],"recommendation":[49],"functions":[50],"provided":[52],"due":[53,290],"difficulty":[56],"uniqueness":[58],"environment,":[62],"which":[64,236],"unknown":[65],"groups":[66,202],"choose":[67,311],"based":[69],"on":[70],"with":[71],"whom":[72],"they":[73],"sharing":[75],"experience":[78,307],"group":[81,251],"mood;":[82],"solid":[83],"information":[84],"these":[86,117],"factors":[87,118],"is":[88,267,284],"not":[89],"available.":[90],"In":[91,182],"this":[92,282],"paper,":[93],"we":[94,184],"propose":[95],"session-based":[97],"multivariate":[98],"recurrent":[99],"neural":[100],"network":[101],"(RNN)":[102],"consider":[104],"an":[105],"occasion":[106],"store":[110],"any":[113],"possible":[114],"mood":[115,255],"changes;":[116],"assumed":[120],"be":[122,150],"embedded":[123],"sequential":[126],"data.":[127],"The":[128,224,273],"model":[129,190,225,279],"uses":[130],"metadata":[131],"from":[132,207,257],"previously":[133],"played":[134,151,208],"songs,":[135,209],"conditions":[137],"present,":[138],"performs":[140],"several":[141],"tasks":[142,164],"that":[143,203,238,266,277],"predict":[144],"artists":[148],"at":[152],"t":[154],"t+n.":[156],"It":[157],"outperformed":[158,212,227],"item-kNN":[160,214],"algorithm":[161,215],"prediction":[163],"(offline)":[165],"song":[169],"by":[170,178,216,308],"127.94%":[171],"regarding":[172,180,221],"MAP@20":[173,222],"artist":[177],"142.8%":[179],"MAP@10.":[181],"addition,":[183],"analyzed":[185],"performance":[187],"three":[192],"types":[193],"groups,":[195,199],"namely":[196],"individuals,":[197],"older":[198],"younger":[201],"were":[204],"manually":[205],"categorized":[206],"it":[211,239],"89.59%,":[217],"483.11%,":[218],"50.88%":[220],"respectively.":[223],"also":[226,285],"baselines":[229],"all":[231],"positions":[232],"session,":[235],"demonstrates":[237],"has":[240,275],"potential":[242],"quickly":[244],"capture":[245],"preferences":[247],"target":[250],"recognize":[254],"changes":[256],"sequence.":[259],"We":[260],"have":[261],"launched":[262],"recommender":[264],"system":[265,274,283],"designed":[268],"Japanese":[270],"stores.":[272],"demonstrated":[276],"used":[280],"appliable":[286],"business":[288],"applications":[289],"its":[292],"ability":[293],"handle":[295],"vast":[296],"numbers":[297],"requests":[299],"reasonable":[302],"enhance":[305],"user":[306],"helping":[309],"diverse":[313],"faster.":[315]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}