{"id":"https://openalex.org/W2167564468","doi":"https://doi.org/10.1145/2566486.2568040","title":"CoBaFi","display_name":"CoBaFi","publication_year":2014,"publication_date":"2014-04-07","ids":{"openalex":"https://openalex.org/W2167564468","doi":"https://doi.org/10.1145/2566486.2568040","mag":"2167564468"},"language":"en","primary_location":{"id":"doi:10.1145/2566486.2568040","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2566486.2568040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on World wide web","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/A5080988309","display_name":"Alex Beutel","orcid":"https://orcid.org/0000-0002-5917-2849"},"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":"Alex Beutel","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005840801","display_name":"Kenton Murray","orcid":"https://orcid.org/0000-0002-5628-1003"},"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":"Kenton Murray","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"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":"Christos Faloutsos","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000245150","display_name":"Alexander J. Smola","orcid":"https://orcid.org/0000-0002-7963-4721"},"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":"Alexander J. Smola","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080988309"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":18.145,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.99043166,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"97","last_page":"108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9998999834060669,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9998999834060669,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.996999979019165,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7751123905181885},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6241147518157959},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.5641739964485168},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5379228591918945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5305315256118774},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5286662578582764},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4987151622772217},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.47406595945358276},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.47103622555732727},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46818864345550537},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.46111205220222473},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45636528730392456},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4144333600997925},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.1808207929134369}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7751123905181885},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6241147518157959},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.5641739964485168},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5379228591918945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5305315256118774},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5286662578582764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4987151622772217},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.47406595945358276},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.47103622555732727},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46818864345550537},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.46111205220222473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45636528730392456},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4144333600997925},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.1808207929134369},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2566486.2568040","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2566486.2568040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd international conference on World wide web","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1054610499","display_name":null,"funder_award_id":"CNS-1314632, IIS-0953330, IIS-0916345, IIS-0911032","funder_id":"https://openalex.org/F4320337388","funder_display_name":"Division of Computer and Network Systems"},{"id":"https://openalex.org/G2626716673","display_name":null,"funder_award_id":"DGE-1252522","funder_id":"https://openalex.org/F4320337368","funder_display_name":"Division of Graduate Education"},{"id":"https://openalex.org/G731724515","display_name":null,"funder_award_id":"CNS-1314632, IIS-0953330, IIS-0916345, IIS-0911032","funder_id":"https://openalex.org/F4320337389","funder_display_name":"Division of Information and Intelligent Systems"},{"id":"https://openalex.org/G948678646","display_name":null,"funder_award_id":"W911NF-09-2-0053","funder_id":"https://openalex.org/F4320338295","funder_display_name":"Army Research Laboratory"}],"funders":[{"id":"https://openalex.org/F4320307762","display_name":"International Business Machines Corporation","ror":"https://ror.org/05hh8d621"},{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"},{"id":"https://openalex.org/F4320337368","display_name":"Division of Graduate Education","ror":"https://ror.org/00whkrf32"},{"id":"https://openalex.org/F4320337388","display_name":"Division of Computer and Network Systems","ror":"https://ror.org/02rdzmk74"},{"id":"https://openalex.org/F4320337389","display_name":"Division of Information and Intelligent Systems","ror":"https://ror.org/053a2cp42"},{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W21006490","https://openalex.org/W48643329","https://openalex.org/W1503398984","https://openalex.org/W1527849298","https://openalex.org/W1533473741","https://openalex.org/W1553350739","https://openalex.org/W1850515838","https://openalex.org/W1976618413","https://openalex.org/W1994389483","https://openalex.org/W2001082470","https://openalex.org/W2001259128","https://openalex.org/W2004657139","https://openalex.org/W2018415308","https://openalex.org/W2047756776","https://openalex.org/W2054141820","https://openalex.org/W2056932183","https://openalex.org/W2061873838","https://openalex.org/W2067812998","https://openalex.org/W2080972498","https://openalex.org/W2085040216","https://openalex.org/W2087309226","https://openalex.org/W2089349245","https://openalex.org/W2099111195","https://openalex.org/W2100235073","https://openalex.org/W2101111945","https://openalex.org/W2107107106","https://openalex.org/W2119384858","https://openalex.org/W2128877075","https://openalex.org/W2128999403","https://openalex.org/W2129728285","https://openalex.org/W2135029798","https://openalex.org/W2135194391","https://openalex.org/W2142535891","https://openalex.org/W2148975075","https://openalex.org/W2149510050","https://openalex.org/W2152836620","https://openalex.org/W2153975459","https://openalex.org/W2156338064","https://openalex.org/W2159359879","https://openalex.org/W2159545104","https://openalex.org/W2951664765","https://openalex.org/W2963977107","https://openalex.org/W3143596294","https://openalex.org/W6607690188","https://openalex.org/W6639041904","https://openalex.org/W6680012447"],"related_works":["https://openalex.org/W2116910984","https://openalex.org/W1831103547","https://openalex.org/W1980122825","https://openalex.org/W3213222469","https://openalex.org/W421912185","https://openalex.org/W2058660987","https://openalex.org/W1978539954","https://openalex.org/W2187718068","https://openalex.org/W4210862145","https://openalex.org/W1975059750"],"abstract_inverted_index":{"Given":[0],"a":[1,19,35,52,87,114],"large":[2],"dataset":[3],"of":[4,7,62,70,80,89,126,139,171],"users'":[5],"ratings":[6,71,133,181],"movies,":[8],"what":[9],"is":[10,73],"the":[11,67,76,81,90,96,101,124,157,169],"best":[12],"model":[13,102,116],"to":[14,41,56,75,104,143,155],"accurately":[15,122,161],"predict":[16],"which":[17,94],"movies":[18,45],"person":[20],"will":[21],"like?":[22],"And":[23],"how":[24],"can":[25],"we":[26,50],"prevent":[27],"spammers":[28],"from":[29],"tricking":[30],"our":[31,84,140],"algorithms":[32],"into":[33],"suggesting":[34],"bad":[36],"movie?":[37],"Is":[38],"it":[39],"possible":[40],"infer":[42],"structure":[43,69],"between":[44],"simultaneously?":[46],"In":[47],"this":[48],"paper":[49],"describe":[51],"unified":[53],"Bayesian":[54],"approach":[55],"Collaborative":[57],"Filtering":[58],"that":[59,121],"accomplishes":[60],"all":[61],"these":[63],"goals.":[64],"It":[65],"models":[66,123],"discrete":[68],"and":[72,92,99,117,145,174],"flexible":[74],"often":[77],"non-Gaussian":[78],"shape":[79],"distribution.":[82],"Additionally,":[83],"method":[85],"finds":[86],"co-clustering":[88],"users":[91],"items,":[93],"improves":[95],"model's":[97,141,158],"accuracy":[98],"makes":[100],"robust":[103],"fraud.":[105],"We":[106,112,136,149],"offer":[107],"three":[108],"main":[109],"contributions:":[110],"(1)":[111],"provide":[113,137],"novel":[115],"Gibbs":[118],"sampling":[119],"algorithm":[120],"quirks":[125],"real":[127,152,179],"world":[128,153,180],"ratings,":[129,164],"such":[130],"as":[131],"convex":[132],"distributions.":[134],"(2)":[135],"proof":[138],"robustness":[142],"spam":[144],"anomalous":[146],"behavior.":[147],"(3)":[148],"use":[150],"several":[151],"datasets":[154],"demonstrate":[156],"effectiveness":[159],"in":[160,168,178],"predicting":[162],"user's":[163],"avoiding":[165],"prediction":[166],"skew":[167],"face":[170],"injected":[172],"spam,":[173],"finding":[175],"interesting":[176],"patterns":[177],"data.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}