{"id":"https://openalex.org/W3186710985","doi":"https://doi.org/10.1145/3451391","title":"Bayesian Additive Matrix Approximation for Social Recommendation","display_name":"Bayesian Additive Matrix Approximation for Social Recommendation","publication_year":2021,"publication_date":"2021-07-20","ids":{"openalex":"https://openalex.org/W3186710985","doi":"https://doi.org/10.1145/3451391","mag":"3186710985"},"language":"en","primary_location":{"id":"doi:10.1145/3451391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3451391","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5100725633","display_name":"Huafeng Liu","orcid":"https://orcid.org/0000-0002-7914-6867"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huafeng Liu","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Jing","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025654446","display_name":"Jingxuan Wen","orcid":"https://orcid.org/0009-0000-6705-8918"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingxuan Wen","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064324887","display_name":"Pengyu Xu","orcid":"https://orcid.org/0000-0002-3176-6944"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengyu Xu","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101970582","display_name":"Jian Yu","orcid":"https://orcid.org/0009-0009-9041-7507"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yu","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010561682","display_name":"Michael K. Ng","orcid":"https://orcid.org/0000-0001-6833-5227"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Michael K. Ng","raw_affiliation_strings":["Department of Mathematics, The University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, The University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6631,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87213003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"16","issue":"1","first_page":"1","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","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/T10203","display_name":"Recommender Systems and Techniques","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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9853000044822693,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6860424876213074},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6593196988105774},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6508032083511353},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5617964267730713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5303314924240112},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5237779021263123},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5230646729469299},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5077275037765503},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.497957706451416},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.4621366560459137},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4525243639945984},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4411674439907074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4239272475242615},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.2177673876285553},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.07824230194091797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6860424876213074},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6593196988105774},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6508032083511353},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5617964267730713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303314924240112},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5237779021263123},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5230646729469299},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5077275037765503},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.497957706451416},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.4621366560459137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4525243639945984},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4411674439907074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4239272475242615},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.2177673876285553},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.07824230194091797},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3451391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3451391","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6000000238418579,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1634564638","display_name":null,"funder_award_id":"2017YFC1703506","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G452380918","display_name":null,"funder_award_id":"61822601, 61773050, and 61632004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6856027503","display_name":null,"funder_award_id":"104005583","funder_id":"https://openalex.org/F4320322170","funder_display_name":"University of Hong Kong"},{"id":"https://openalex.org/G8377820436","display_name":null,"funder_award_id":"2019JBZ110","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322170","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W950821216","https://openalex.org/W1965688895","https://openalex.org/W1966472199","https://openalex.org/W1971788485","https://openalex.org/W1984127251","https://openalex.org/W1993897382","https://openalex.org/W2003687960","https://openalex.org/W2009205701","https://openalex.org/W2016666840","https://openalex.org/W2033765726","https://openalex.org/W2048531216","https://openalex.org/W2054141820","https://openalex.org/W2070263508","https://openalex.org/W2085040216","https://openalex.org/W2093219534","https://openalex.org/W2119825970","https://openalex.org/W2133266261","https://openalex.org/W2135598826","https://openalex.org/W2139694940","https://openalex.org/W2144487656","https://openalex.org/W2148292526","https://openalex.org/W2149490995","https://openalex.org/W2159094788","https://openalex.org/W2160547390","https://openalex.org/W2164928285","https://openalex.org/W2171813380","https://openalex.org/W2266294848","https://openalex.org/W2343271409","https://openalex.org/W2343644403","https://openalex.org/W2358258772","https://openalex.org/W2466547271","https://openalex.org/W2509678028","https://openalex.org/W2536436157","https://openalex.org/W2605350416","https://openalex.org/W2613709413","https://openalex.org/W2621465063","https://openalex.org/W2624298973","https://openalex.org/W2746264214","https://openalex.org/W2914721378","https://openalex.org/W2963085847","https://openalex.org/W2972734859","https://openalex.org/W2981260853","https://openalex.org/W2997313755","https://openalex.org/W3012805373","https://openalex.org/W3123329971","https://openalex.org/W4301312111"],"related_works":["https://openalex.org/W1517763961","https://openalex.org/W2133543800","https://openalex.org/W3122927555","https://openalex.org/W2494929626","https://openalex.org/W27102384","https://openalex.org/W1990075812","https://openalex.org/W4378501308","https://openalex.org/W2952528636","https://openalex.org/W4243703723","https://openalex.org/W1546666705"],"abstract_inverted_index":{"Social":[0],"relations":[1,31],"between":[2],"users":[3,154],"have":[4],"been":[5],"proven":[6],"to":[7,15,26,66,76,85,115,169],"be":[8],"a":[9,23,49],"good":[10],"type":[11],"of":[12,60,73,122,136,162],"auxiliary":[13],"information":[14],"improve":[16],"the":[17,29,35,92,117,134,137,143,160,171],"recommendation":[18],"performance.":[19],"However,":[20],"it":[21],"is":[22,75,113],"challenging":[24],"issue":[25],"sufficiently":[27],"exploit":[28],"social":[30,40,64,78,81,129,157,163],"and":[32,41,63,95,98,153],"correctly":[33],"determine":[34],"user":[36,93],"preference":[37,62],"from":[38,80,146],"both":[39],"rating":[42,61,102],"information.":[43],"In":[44,104],"this":[45],"article,":[46],"we":[47],"propose":[48],"unified":[50],"Bayesian":[51,86],"Additive":[52],"Matrix":[53],"Approximation":[54],"model":[55],"(BAMA),":[56],"which":[57],"takes":[58],"advantage":[59],"network":[65],"provide":[67,99,170],"high-quality":[68],"recommendation.":[69,173],"The":[70,131],"basic":[71],"idea":[72],"BAMA":[74,139,166],"extract":[77],"influence":[79],"networks,":[82],"integrate":[83],"them":[84],"additive":[87],"co-clustering":[88],"for":[89],"effectively":[90],"determining":[91],"clusters":[94],"item":[96],"clusters,":[97],"an":[100,106],"accurate":[101],"prediction.":[103],"addition,":[105],"efficient":[107],"algorithm":[108],"with":[109,142,155],"collapsed":[110],"Gibbs":[111],"Sampling":[112],"designed":[114],"inference":[116],"proposed":[118,138],"model.":[119],"A":[120],"series":[121],"experiments":[123],"were":[124],"conducted":[125],"on":[126],"six":[127],"real-world":[128],"datasets.":[130],"results":[132],"demonstrate":[133],"superiority":[135],"by":[140],"comparing":[141],"state-of-the-art":[144],"methods":[145],"three":[147],"views,":[148],"all":[149],"users,":[150,152],"cold-start":[151],"few":[156],"relations.":[158],"With":[159],"aid":[161],"information,":[164],"furthermore,":[165],"has":[167],"ability":[168],"explainable":[172]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}