{"id":"https://openalex.org/W2117420919","doi":"https://doi.org/10.1145/1401890.1401969","title":"Relational learning via collective matrix factorization","display_name":"Relational learning via collective matrix factorization","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W2117420919","doi":"https://doi.org/10.1145/1401890.1401969","mag":"2117420919"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401969","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5101971440","display_name":"Ajit Singh","orcid":"https://orcid.org/0000-0002-5532-2803"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ajit P. Singh","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012830032","display_name":"Geoffrey J. Gordon","orcid":"https://orcid.org/0009-0007-9960-2114"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Geoffrey J. Gordon","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101971440"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":19.1101,"has_fulltext":false,"cited_by_count":1225,"citation_normalized_percentile":{"value":0.9959169,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"650","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7099233865737915},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6697161197662354},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6379958391189575},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6096746325492859},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.4820474088191986},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.4606930911540985},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.45597657561302185},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4442426562309265},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4339643716812134},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4146282374858856},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.411787748336792},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.40867218375205994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39076685905456543},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35104119777679443},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3247714042663574},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31242817640304565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18807438015937805}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7099233865737915},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6697161197662354},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6379958391189575},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6096746325492859},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.4820474088191986},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.4606930911540985},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.45597657561302185},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4442426562309265},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4339643716812134},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4146282374858856},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.411787748336792},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.40867218375205994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39076685905456543},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35104119777679443},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3247714042663574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31242817640304565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18807438015937805},{"id":"https://openalex.org/C25023664","wikidata":"https://www.wikidata.org/wiki/Q1575637","display_name":"Hankel matrix","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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":5,"locations":[{"id":"doi:10.1145/1401890.1401969","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:ADA487940","is_oa":false,"landing_page_url":"http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA487940","pdf_url":null,"source":{"id":"https://openalex.org/S4406923043","display_name":"Defense Technical Information Center (DTIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"DTIC","raw_type":"Text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.6607","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.6607","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://reports-archive.adm.cs.cmu.edu/anon/ml2008/CMU-ML-08-109.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.7480","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.7480","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~ggordon/singh-gordon-kdd-factorization.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.210.9571","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.9571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/%7Eajit/cmf/kdd439-singh.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W16591383","https://openalex.org/W263845233","https://openalex.org/W328516003","https://openalex.org/W1506313179","https://openalex.org/W1528905581","https://openalex.org/W1544068237","https://openalex.org/W1556598185","https://openalex.org/W1568229137","https://openalex.org/W1586776267","https://openalex.org/W1601530222","https://openalex.org/W1921200167","https://openalex.org/W1976618413","https://openalex.org/W1979584682","https://openalex.org/W2018415308","https://openalex.org/W2032459394","https://openalex.org/W2033468335","https://openalex.org/W2054877413","https://openalex.org/W2056932183","https://openalex.org/W2096765209","https://openalex.org/W2102486516","https://openalex.org/W2103731208","https://openalex.org/W2106652568","https://openalex.org/W2107743791","https://openalex.org/W2108346334","https://openalex.org/W2111101775","https://openalex.org/W2122090912","https://openalex.org/W2127137551","https://openalex.org/W2135001774","https://openalex.org/W2135029798","https://openalex.org/W2138180870","https://openalex.org/W2144192824","https://openalex.org/W2146012283","https://openalex.org/W2165395308","https://openalex.org/W2296319761","https://openalex.org/W2797638056","https://openalex.org/W2945526390","https://openalex.org/W2976223356","https://openalex.org/W3029645440","https://openalex.org/W3143596294","https://openalex.org/W4233135949","https://openalex.org/W4254223024","https://openalex.org/W4293775970","https://openalex.org/W4301861531","https://openalex.org/W6611379484","https://openalex.org/W6632650772","https://openalex.org/W6675672627","https://openalex.org/W6677671969","https://openalex.org/W6680012447","https://openalex.org/W6684295950"],"related_works":["https://openalex.org/W2554601002","https://openalex.org/W3142085906","https://openalex.org/W2125326641","https://openalex.org/W2054286941","https://openalex.org/W210173153","https://openalex.org/W59193195","https://openalex.org/W4288429585","https://openalex.org/W1504001548","https://openalex.org/W958387121","https://openalex.org/W2129340519"],"abstract_inverted_index":{"Relational":[0],"learning":[1,25],"is":[2,26,68],"concerned":[3],"with":[4,74,174],"predicting":[5,93],"unknown":[6],"values":[7],"of":[8,14,23,43,208,220],"a":[9,12,62,100,125,205],"relation,":[10,59],"given":[11,56],"database":[13],"entities":[15,31],"and":[16,37,47,129,140,157,186,204],"observed":[17],"relations":[18],"among":[19,112,223],"entities.":[20],"An":[21],"example":[22,61],"relational":[24,202],"movie":[27],"rating":[28],"prediction,":[29],"where":[30],"could":[32],"include":[33],"users,":[34],"movies,":[35,44],"genres,":[36,46],"actors.":[38],"Relations":[39],"encode":[40],"users'":[41],"ratings":[42,66],"movies'":[45],"actors'":[48],"roles":[49],"in":[50,118],"movies.":[51],"A":[52],"common":[53],"prediction":[54],"technique":[55],"one":[57,90],"pairwise":[58,201],"for":[60,163,193],"#users":[63],"x":[64],"#movies":[65],"matrix,":[67],"low-rank":[69],"matrix":[70,102,183],"factorization.":[71],"In":[72],"domains":[73],"multiple":[75,79,119],"relations,":[76],"represented":[77],"as":[78,215,217],"matrices,":[80,109],"we":[81,98,105,133,167],"may":[82],"improve":[83],"predictive":[84],"accuracy":[85],"by":[86],"exploiting":[87],"information":[88],"from":[89],"relation":[91,122],"while":[92],"another.":[94],"To":[95],"this":[96],"end,":[97],"propose":[99,168],"collective":[101],"factorization":[103,184],"model:":[104],"simultaneously":[106],"factor":[107],"several":[108,181],"sharing":[110,221],"parameters":[111,139,222],"factors":[113],"when":[114],"an":[115,159],"entity":[116],"participates":[117],"relations.":[120,224],"Each":[121],"can":[123,198],"have":[124],"different":[126],"value":[127],"type":[128],"error":[130,209],"distribution;":[131],"so,":[132],"allow":[134],"nonlinear":[135],"relationships":[136],"between":[137],"the":[138,164,218],"outputs,":[141],"using":[142],"Bregman":[143],"divergences":[144],"to":[145,154,172],"measure":[146],"error.":[147],"We":[148,211],"extend":[149],"standard":[150],"alternating":[151],"projection":[152],"algorithms":[153,192],"our":[155],"model,":[156],"derive":[158],"efficient":[160],"Newton":[161],"update":[162],"projection.":[165],"Furthermore,":[166],"stochastic":[169],"optimization":[170,191],"methods":[171],"deal":[173],"large,":[175],"sparse":[176],"matrices.":[177],"Our":[178,196],"model":[179,197],"generalizes":[180],"existing":[182],"methods,":[185],"therefore":[187],"yields":[188],"new":[189],"large-scale":[190],"these":[194],"problems.":[195],"handle":[199],"any":[200],"schema":[203],"wide":[206],"variety":[207],"models.":[210],"demonstrate":[212],"its":[213],"efficiency,":[214],"well":[216],"benefit":[219]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":66},{"year":2024,"cited_by_count":69},{"year":2023,"cited_by_count":70},{"year":2022,"cited_by_count":63},{"year":2021,"cited_by_count":83},{"year":2020,"cited_by_count":121},{"year":2019,"cited_by_count":109},{"year":2018,"cited_by_count":105},{"year":2017,"cited_by_count":110},{"year":2016,"cited_by_count":97},{"year":2015,"cited_by_count":90},{"year":2014,"cited_by_count":69},{"year":2013,"cited_by_count":48},{"year":2012,"cited_by_count":42}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}