{"id":"https://openalex.org/W2552723767","doi":null,"title":"Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering","display_name":"Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2552723767","mag":"2552723767"},"language":"en","primary_location":{"id":"mag:2552723767","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/6108-blind-regression-nonparametric-regression-for-latent-variable-models-via-collaborative-filtering.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5056798043","display_name":"Dogyoon Song","orcid":"https://orcid.org/0000-0001-5489-8213","observed_orcids":["https://orcid.org/0000-0001-5489-8213"]},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dogyoon Song","raw_affiliation_strings":["Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676442","display_name":"Christina Lee","orcid":"https://orcid.org/0000-0002-2127-8453","observed_orcids":["https://orcid.org/0000-0002-2127-8453"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christina Lee","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, United States"],"raw_orcid":"https://orcid.org/0000-0002-2127-8453","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, United States","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101573521","display_name":"Yihua Li","orcid":"https://orcid.org/0000-0002-9129-8062","observed_orcids":["https://orcid.org/0000-0002-9129-8062"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihua Li","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, United States"],"raw_orcid":"https://orcid.org/0000-0002-9129-8062","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, United States","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029499294","display_name":"Devavrat Shah","orcid":"https://orcid.org/0000-0003-0737-3259","observed_orcids":["https://orcid.org/0000-0003-0737-3259"]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Devavrat Shah","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge, United States"],"raw_orcid":"https://orcid.org/0000-0003-0737-3259","affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, United States","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.8789,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.98079174,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"29","issue":null,"first_page":"2155","last_page":"2163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9657999873161316,"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":0.9657999873161316,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.003599999938160181,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0034000000450760126,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.8450000286102295},{"id":"https://openalex.org/keywords/latent-variable-modeling","display_name":"latent variable models","score":0.9350000023841858},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"collaborative filtering","score":0.9079999923706055},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"matrix completion","score":0.9990000128746033},{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.8960000276565552},{"id":"https://openalex.org/keywords/lipschitz-functions","display_name":"Lipschitz functions","score":0.8510000109672546},{"id":"https://openalex.org/keywords/taylor-expansion","display_name":"Taylor series expansion","score":0.8539999723434448},{"id":"https://openalex.org/keywords/forecast-consistency","display_name":"prediction consistency","score":0.6129999756813049},{"id":"https://openalex.org/keywords/matrix-factorization","display_name":"matrix factorization","score":0.6819999814033508}],"concepts":[{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.809785783290863},{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.6313741207122803},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.521612286567688},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5092774629592896},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5081288814544678},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.49849939346313477},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.4897747039794922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4607596695423126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45016512274742126},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4372536540031433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42994609475135803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4135921001434326},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.41039231419563293},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3396924138069153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3000723719596863},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2552723767","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/6108-blind-regression-nonparametric-regression-for-latent-variable-models-via-collaborative-filtering.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W198034206","https://openalex.org/W1916337001","https://openalex.org/W1966553486","https://openalex.org/W1977877565","https://openalex.org/W1996023858","https://openalex.org/W2059283452","https://openalex.org/W2084745075","https://openalex.org/W2106005123","https://openalex.org/W2111297856","https://openalex.org/W2144730813","https://openalex.org/W2146130798","https://openalex.org/W2152836620","https://openalex.org/W2167686991","https://openalex.org/W2185212216","https://openalex.org/W2611328865"],"related_works":["https://openalex.org/W2987348629","https://openalex.org/W2616154752","https://openalex.org/W7064621821","https://openalex.org/W7166033154","https://openalex.org/W2789125529","https://openalex.org/W7009778893","https://openalex.org/W2478976345","https://openalex.org/W2016515835","https://openalex.org/W2765123643","https://openalex.org/W7098528890"],"abstract_inverted_index":{"We":[0],"introduce":[1],"the":[2,27,32,37,42,47,88,107,112,133,149,175,200,203,210],"framework":[3,48,137],"of":[4,24,49,75,143,152,178,202],"{\\em":[5,10],"blind":[6],"regression}":[7],"motivated":[8],"by":[9,111],"matrix":[11,230],"completion}":[12],"for":[13,78,116,127],"recommendation":[14],"systems:":[15],"given":[16,36],"$m$":[17],"users,":[18],"$n$":[19],"movies,":[20],"and":[21,57,63,66,212,226],"a":[22,72,121,141,193],"subset":[23],"user-movie":[25,34],"ratings,":[26],"goal":[28],"is":[29,71,125,160,186,199,227],"to":[30,40,100,105,140],"predict":[31,106],"unobserved":[33,108],"ratings":[35],"data,":[38],"i.e.,":[39],"complete":[41],"partially":[43],"observed":[44],"matrix.":[45],"Following":[46],"non-parametric":[50],"statistics,":[51],"we":[52,119,172],"posit":[53],"that":[54,124,174,216],"user":[55],"$u$":[56],"movie":[58],"$i$":[59],"have":[60],"features":[61,89],"$x_1(u)$":[62],"$x_2(i)$":[64],"respectively,":[65],"their":[67],"corresponding":[68],"rating":[69],"$y(u,i)$":[70],"noisy":[73],"measurement":[74],"$f(x_1(u),":[76],"x_2(i))$":[77,93],"some":[79],"unknown":[80],"function":[81],"$f$.":[82],"In":[83,131],"contrast":[84],"with":[85,163,168,181,209,229],"classical":[86,113],"regression,":[87],"$x":[90],"=":[91],"(x_1(u),":[92],"are":[94],"not":[95],"observed,":[96],"making":[97],"it":[98],"challenging":[99],"apply":[101],"standard":[102],"regression":[103],"methods":[104],"ratings.":[109],"Inspired":[110],"Taylor's":[114],"expansion":[115],"differentiable":[117],"functions,":[118],"provide":[120],"prediction":[122],"algorithm":[123,218],"consistent":[126],"all":[128],"Lipschitz":[129],"functions.":[130],"fact,":[132],"analysis":[134],"through":[135],"our":[136,179,217],"naturally":[138],"leads":[139],"variant":[142],"collaborative":[144,153,224],"filtering,":[145],"shedding":[146],"insight":[147],"into":[148],"widespread":[150],"success":[151],"filtering":[154,225],"in":[155],"practice.":[156],"Assuming":[157],"each":[158],"entry":[159],"sampled":[161],"independently":[162],"probability":[164],"at":[165],"least":[166],"$\\max(m^{-1+\\delta},n^{-1/2+\\delta})$":[167],"$\\delta":[169],">":[170],"0$,":[171],"prove":[173],"expected":[176],"fraction":[177],"estimates":[180],"error":[182],"greater":[183],"than":[184,188],"$\\epsilon$":[185],"less":[187],"$\\gamma^2":[189],"/":[190],"\\epsilon^2$":[191],"plus":[192],"polynomially":[194],"decaying":[195],"term,":[196],"where":[197],"$\\gamma^2$":[198],"variance":[201],"additive":[204],"entry-wise":[205],"noise":[206],"term.":[207],"Experiments":[208],"MovieLens":[211],"Netflix":[213],"datasets":[214],"suggest":[215],"provides":[219],"principled":[220],"improvements":[221],"over":[222],"basic":[223],"competitive":[228],"factorization":[231],"methods.":[232]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":6}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2016-11-30T00:00:00"}
