{"id":"https://openalex.org/W2574897237","doi":null,"title":"Bayesian probabilistic multi-topic matrix factorization for rating prediction","display_name":"Bayesian probabilistic multi-topic matrix factorization for rating prediction","publication_year":2016,"publication_date":"2016-07-09","ids":{"openalex":"https://openalex.org/W2574897237","mag":"2574897237"},"language":"en","primary_location":{"id":"mag:2574897237","doi":null,"is_oa":false,"landing_page_url":"http://dblp.uni-trier.de/db/conf/ijcai/ijcai2016.html#WangZPW16","pdf_url":null,"source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"International Joint Conference on Artificial Intelligence","raw_type":null},"volume":null,"type":"conference-paper","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/A5022946499","display_name":"Keqiang Wang","orcid":"https://orcid.org/0000-0002-5151-0372","observed_orcids":["https://orcid.org/0000-0002-5151-0372"]},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keqiang Wang","raw_affiliation_strings":["Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-5151-0372","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037145565","display_name":"Wayne Xin Zhao","orcid":"https://orcid.org/0000-0002-8333-6196","observed_orcids":["https://orcid.org/0000-0002-8333-6196"]},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wayne Xin Zhao","raw_affiliation_strings":["[School of information, Renmin University of China, China]"],"raw_orcid":"https://orcid.org/0000-0002-8333-6196","affiliations":[{"raw_affiliation_string":"[School of information, Renmin University of China, China]","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089332186","display_name":"Peng Hong-wei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Hong-wei","raw_affiliation_strings":["Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100344628","display_name":"Xiaoling Wang","orcid":"https://orcid.org/0000-0002-4594-6946","observed_orcids":["https://orcid.org/0000-0002-4594-6946"]},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoling Wang","raw_affiliation_strings":["Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-4594-6946","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9251,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86791055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3910","last_page":"3916"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9980000257492065,"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":0.9980000257492065,"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/T10028","display_name":"Topic Modeling","score":0.00019999999494757503,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":9.999999747378752e-05,"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/rating-prediction","display_name":"rating prediction","score":0.8999999761581421},{"id":"https://openalex.org/keywords/bayesian-probabilistic-model","display_name":"Bayesian probabilistic model","score":0.6990000009536743},{"id":"https://openalex.org/keywords/latent-topic-models","display_name":"latent topic models","score":0.9679999947547913},{"id":"https://openalex.org/keywords/item-clustering","display_name":"item clustering","score":0.722000002861023},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"ensemble forecasting","score":0.8849999904632568},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"collaborative filtering","score":0.6669999957084656},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.7409999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143381834030151},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6900351047515869},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6763145327568054},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5625759363174438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5152761936187744},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5147573947906494},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4982929229736328},{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.4953388273715973},{"id":"https://openalex.org/C85817219","wikidata":"https://www.wikidata.org/wiki/Q884772","display_name":"Block matrix","level":3,"score":0.4883527457714081},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47885677218437195},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43724098801612854},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39479461312294006},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.20072853565216064},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.08815056085586548},{"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2574897237","doi":null,"is_oa":false,"landing_page_url":"http://dblp.uni-trier.de/db/conf/ijcai/ijcai2016.html#WangZPW16","pdf_url":null,"source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"International Joint Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1880262756","https://openalex.org/W1965688895","https://openalex.org/W2001082470","https://openalex.org/W2004445826","https://openalex.org/W2016666840","https://openalex.org/W2045882407","https://openalex.org/W2054141820","https://openalex.org/W2061873838","https://openalex.org/W2085040216","https://openalex.org/W2101409192","https://openalex.org/W2110591510","https://openalex.org/W2117309679","https://openalex.org/W2135194391","https://openalex.org/W2135790056","https://openalex.org/W2152184085","https://openalex.org/W2169661502","https://openalex.org/W2171813380","https://openalex.org/W2405923393","https://openalex.org/W2434205482","https://openalex.org/W2963363076"],"related_works":["https://openalex.org/W2768077399","https://openalex.org/W2514530580","https://openalex.org/W3106302634","https://openalex.org/W2160385855","https://openalex.org/W3159965639","https://openalex.org/W2085040216","https://openalex.org/W2277352357","https://openalex.org/W2788893025","https://openalex.org/W1712913416","https://openalex.org/W3180220954"],"abstract_inverted_index":{"Recently,":[0],"Local":[1],"Matrix":[2,78],"Factorization":[3,79],"(LMF)":[4],"[Lee":[5],"et":[6],"al.,":[7],"2013]":[8],"has":[9,108],"been":[10,56],"shown":[11],"to":[12,29,100,157],"be":[13],"more":[14,66,155],"effective":[15],"than":[16],"traditional":[17],"matrix":[18,34],"factorization":[19],"for":[20,26,45,58,69,122,162],"rating":[21,167],"prediction.":[22],"The":[23,127],"core":[24],"idea":[25],"LMF":[27,59],"is":[28,130,154],"first":[30],"partition":[31],"the":[32,61,83,86,112,136,139,159,171,181,184],"original":[33],"into":[35],"several":[36],"smaller":[37],"submatrices,":[38],"further":[39,117],"exploit":[40],"local":[41],"structures":[42],"of":[43,85,114,135,183],"submatrices":[44],"better":[46],"low-rank":[47],"approximation.":[48],"Various":[49],"clustering-based":[50],"methods":[51],"with":[52],"heuristic":[53],"extensions":[54],"have":[55],"proposed":[57,185],"in":[60,144,166],"literature.":[62],"To":[63],"develop":[64],"a":[65,74,90,93,106,109,148],"principled":[67],"solution":[68],"LMF,":[70],"this":[71],"paper":[72],"presents":[73],"Bayesian":[75],"Probabilistic":[76],"Multi-Topic":[77],"model.":[80,186],"We":[81,116],"treat":[82],"set":[84,113,118],"rated":[87],"items":[88,102,165],"by":[89,132],"user":[91,107],"as":[92,103],"document,":[94],"and":[95,125,164,169],"employ":[96],"latent":[97,120,142,150],"topic":[98],"models":[99],"cluster":[101],"topics.":[104,115],"Subsequently,":[105],"distribution":[110],"over":[111],"topic-specific":[119,141],"vectors":[121,143],"both":[123],"users":[124,163],"items.":[126],"final":[128],"prediction":[129],"obtained":[131],"an":[133],"ensemble":[134],"results":[137],"from":[138],"corresponding":[140],"each":[145],"topic.":[146],"Using":[147],"multi-topic":[149],"representation,":[151],"our":[152],"model":[153,172],"powerful":[156],"reflect":[158],"complex":[160],"characteristics":[161],"prediction,":[168],"enhance":[170],"interpretability.":[173],"Extensive":[174],"experiments":[175],"on":[176],"large":[177],"real-world":[178],"datasets":[179],"demonstrate":[180],"effectiveness":[182]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2017-01-26T00:00:00"}
