{"id":"https://openalex.org/W2069589188","doi":"https://doi.org/10.1145/2806416.2806636","title":"Probabilistic Non-negative Inconsistent-resolution Matrices Factorization","display_name":"Probabilistic Non-negative Inconsistent-resolution Matrices Factorization","publication_year":2015,"publication_date":"2015-10-17","ids":{"openalex":"https://openalex.org/W2069589188","doi":"https://doi.org/10.1145/2806416.2806636","mag":"2069589188"},"language":"en","primary_location":{"id":"doi:10.1145/2806416.2806636","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2806416.2806636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","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/A5088294917","display_name":"Masahiro Kohjima","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":"Masahiro Kohjima","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001502780","display_name":"Tatsushi Matsubayashi","orcid":"https://orcid.org/0009-0004-2793-8266"},"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":"Tatsushi Matsubayashi","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007032557","display_name":"Hiroshi Sawada","orcid":"https://orcid.org/0000-0002-4831-9286"},"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":"Hiroshi Sawada","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088294917"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.7364,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.78916601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1855","last_page":"1858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9778000116348267,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9778000116348267,"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/T11106","display_name":"Data Management and Algorithms","score":0.9765999913215637,"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/T10057","display_name":"Face and Expression Recognition","score":0.9757999777793884,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7948219776153564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7274191379547119},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.6948215961456299},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.568971574306488},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5606318116188049},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5063408613204956},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4989597797393799},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.45200955867767334},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4290359318256378},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4062947928905487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35248905420303345},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2947767376899719},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10290881991386414}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7948219776153564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7274191379547119},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.6948215961456299},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.568971574306488},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5606318116188049},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5063408613204956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4989597797393799},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.45200955867767334},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4290359318256378},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4062947928905487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35248905420303345},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2947767376899719},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10290881991386414},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2806416.2806636","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2806416.2806636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","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":13,"referenced_works":["https://openalex.org/W76897483","https://openalex.org/W192994201","https://openalex.org/W1246381107","https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1902027874","https://openalex.org/W2070739134","https://openalex.org/W2117420919","https://openalex.org/W2144750001","https://openalex.org/W2404400936","https://openalex.org/W2530593441","https://openalex.org/W6607907923","https://openalex.org/W6639619044"],"related_works":["https://openalex.org/W2943623134","https://openalex.org/W2494523064","https://openalex.org/W2215759665","https://openalex.org/W2960358116","https://openalex.org/W2030292806","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098","https://openalex.org/W4287727129","https://openalex.org/W2938171715"],"abstract_inverted_index":{"In":[0,45],"this":[1,53],"paper,":[2],"we":[3,55],"tackle":[4],"with":[5,11,143],"the":[6,58,79,102,110,114,134,138],"problem":[7,139],"of":[8,18,116,126,140],"analyzing":[9,141],"datasets":[10],"different":[12,144],"resolution":[13],"such":[14],"as":[15,113],"a":[16,49,63],"pair":[17],"user's":[19],"individual":[20],"data":[21,80,86,98,118,131],"and":[22,33,61,99,108],"user":[23],"group's":[24],"data,":[25],"for":[26],"example":[27],"\"userA":[28],"visited":[29],"shopA":[30],"5":[31],"times\"":[32],"\"users":[34],"whose":[35],"attributes":[36],"are":[37],"men":[38],"purchased":[39],"itemA":[40],"80":[41],"times":[42],"in":[43],"total\".":[44],"order":[46],"to":[47,52,137],"establish":[48],"basic":[50,135],"approach":[51,136],"problem,":[54],"focus":[56],"on":[57,94],"simplified":[59],"scenario":[60],"propose":[62],"new":[64],"probabilistic":[65,68],"model":[66,104],"called":[67],"non-negative":[69],"inconsistent-resolution":[70],"matrices":[71],"factorization":[72],"(pNimf).":[73],"pNimf":[74],"is":[75],"rigorously":[76],"derived":[77],"from":[78],"generative":[81],"process":[82],"using":[83,128],"latent":[84,129],"high-resolution":[85,130],"which":[87],"underlie":[88],"low-resolution":[89,117],"data.":[90],"We":[91],"conduct":[92],"experiments":[93],"real":[95],"purchase":[96],"log":[97],"confirm":[100],"that":[101,109,123],"proposed":[103],"provides":[105],"superior":[106],"performance,":[107],"performance":[111],"improves":[112],"number":[115],"increases.":[119],"These":[120],"results":[121],"imply":[122],"our":[124],"way":[125],"modeling":[127],"can":[132],"become":[133],"dataset":[142],"resolution.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}