{"id":"https://openalex.org/W4293248026","doi":"https://doi.org/10.1145/3539813.3545147","title":"Two-sided Rank Consistent Ordinal Regression for Interpretable Music Key Recommendation","display_name":"Two-sided Rank Consistent Ordinal Regression for Interpretable Music Key Recommendation","publication_year":2022,"publication_date":"2022-08-23","ids":{"openalex":"https://openalex.org/W4293248026","doi":"https://doi.org/10.1145/3539813.3545147"},"language":"en","primary_location":{"id":"doi:10.1145/3539813.3545147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539813.3545147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval","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/A5100344854","display_name":"Yuan Wang","orcid":"https://orcid.org/0000-0002-4934-4141"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuan Wang","raw_affiliation_strings":["Santa Clara University, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Santa Clara University, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I16269868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109501933","display_name":"Shigeki Tanaka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shigeki Tanaka","raw_affiliation_strings":["NTT DOCOMO, INC., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034226803","display_name":"Keita Yokoyama","orcid":"https://orcid.org/0000-0001-5329-3298"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keita Yokoyama","raw_affiliation_strings":["NTT DOCOMO, INC., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT DOCOMO, INC., Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061883860","display_name":"Hsin-Tai Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsin-Tai Wu","raw_affiliation_strings":["DOCOMO Innovations, Inc., Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"DOCOMO Innovations, Inc., Palo Alto, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101972978","display_name":"Yi Fang","orcid":"https://orcid.org/0000-0001-6572-4315"},"institutions":[{"id":"https://openalex.org/I16269868","display_name":"Santa Clara University","ror":"https://ror.org/03ypqe447","country_code":"US","type":"education","lineage":["https://openalex.org/I16269868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Fang","raw_affiliation_strings":["Santa Clara University, Santa Clara, CA, USA"],"affiliations":[{"raw_affiliation_string":"Santa Clara University, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I16269868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100344854"],"corresponding_institution_ids":["https://openalex.org/I16269868"],"apc_list":null,"apc_paid":null,"fwci":0.1486,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39372998,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"223","last_page":"231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9997000098228455,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.998199999332428,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8173562288284302},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7617931365966797},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.7422136068344116},{"id":"https://openalex.org/keywords/ordinal-regression","display_name":"Ordinal regression","score":0.5611793398857117},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5507517457008362},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5149884819984436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4729977250099182},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4440048635005951},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42979276180267334},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14998602867126465},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10668429732322693}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8173562288284302},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7617931365966797},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.7422136068344116},{"id":"https://openalex.org/C110313322","wikidata":"https://www.wikidata.org/wiki/Q7100793","display_name":"Ordinal regression","level":2,"score":0.5611793398857117},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5507517457008362},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5149884819984436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4729977250099182},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4440048635005951},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42979276180267334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14998602867126465},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10668429732322693},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539813.3545147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539813.3545147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval","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":29,"referenced_works":["https://openalex.org/W1969566388","https://openalex.org/W1971460272","https://openalex.org/W2048657872","https://openalex.org/W2058475745","https://openalex.org/W2115843962","https://openalex.org/W2117311203","https://openalex.org/W2131774270","https://openalex.org/W2147232804","https://openalex.org/W2171960770","https://openalex.org/W2295739661","https://openalex.org/W2440214111","https://openalex.org/W2517095612","https://openalex.org/W2593380430","https://openalex.org/W2604272474","https://openalex.org/W2612690371","https://openalex.org/W2798435682","https://openalex.org/W2888034815","https://openalex.org/W2921937102","https://openalex.org/W2954902138","https://openalex.org/W2962807820","https://openalex.org/W2970271284","https://openalex.org/W3013870856","https://openalex.org/W3097760447","https://openalex.org/W3101366597","https://openalex.org/W3103101828","https://openalex.org/W3134983687","https://openalex.org/W4212774754","https://openalex.org/W4234552385","https://openalex.org/W4254181784"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W3160516639","https://openalex.org/W2030978506"],"abstract_inverted_index":{"Model":[0],"interpretability":[1],"has":[2,35,179],"attracted":[3],"increasing":[4],"attention":[5],"in":[6,39,71,122,136],"the":[7,24,27,30,50,55,60,67,90,94,102,114,126,129,137,157,165,168,172,180,184,189,196,204,210,215],"IR":[8],"community":[9],"since":[10],"it":[11,46,177],"is":[12,112,159],"important":[13],"to":[14,64,86,108,163],"ensure":[15],"that":[16,89,156],"end-users":[17],"(decision-makers)":[18],"correctly":[19],"understand":[20],"and":[21,42,199],"consequently":[22],"trust":[23],"functionality":[25],"of":[26,217],"models.":[28],"On":[29],"other":[31],"hand,":[32],"ordinal":[33],"regression":[34],"been":[36],"widely":[37],"used":[38],"many":[40],"ranking":[41],"prediction":[43],"tasks,":[44],"but":[45,176],"could":[47,79,92],"not":[48,160],"guarantee":[49],"rank":[51],"consistent":[52],"predictions":[53],"for":[54,98,105,167,183],"output":[56],"labels,":[57],"which":[58],"makes":[59],"predicted":[61],"results":[62,208],"hard":[63],"explain.":[65],"Take":[66],"music":[68],"key":[69,82,104,166,201],"recommendation":[70],"karaoke":[72,212],"as":[73],"an":[74],"example":[75],"where":[76],"a":[77,81,106,110,146,192,200],"user":[78,107],"select":[80],"ranging":[83],"from":[84],"-7":[85],"+7":[87],"so":[88],"song":[91,111,170],"meet":[93],"user's":[95,173],"vocal":[96],"competence":[97],"better":[99],"performance.":[100],"If":[101],"best":[103],"sing":[109],"-3,":[113],"keys":[115,127],"smaller":[116],"than":[117],"-3":[118],"should":[119,132],"be":[120,134],"ranked":[121,135],"decreasing":[123,138],"order.":[124,139],"Similarly,":[125],"on":[128,209],"positive":[130],"side":[131],"also":[133,178],"To":[140],"address":[141],"this":[142],"challenge,":[143],"we":[144],"propose":[145],"novel":[147],"Two-sided":[148],"Rank":[149],"Consistent":[150],"Ordinal":[151],"Regression":[152],"model.":[153,220],"We":[154,187],"show":[155],"model":[158,190],"only":[161],"able":[162],"predict":[164],"target":[169],"given":[171],"singing":[174],"history,":[175],"theoretical":[181],"guarantees":[182],"two-sided":[185],"rank-monotonicity.":[186],"train":[188],"with":[191],"history":[193],"encoder":[194],"using":[195,203],"recurrent":[197],"units":[198],"decoder":[202],"Transformer.":[205],"The":[206],"experimental":[207],"real-world":[211],"dataset":[213],"demonstrate":[214],"effectiveness":[216],"our":[218],"proposed":[219]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}