{"id":"https://openalex.org/W2513818136","doi":"https://doi.org/10.1145/2959100.2959132","title":"Efficient Bayesian Methods for Graph-based Recommendation","display_name":"Efficient Bayesian Methods for Graph-based Recommendation","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2513818136","doi":"https://doi.org/10.1145/2959100.2959132","mag":"2513818136"},"language":"en","primary_location":{"id":"doi:10.1145/2959100.2959132","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2959100.2959132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Conference on Recommender Systems","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/A5101766026","display_name":"Ramon Lopes","orcid":"https://orcid.org/0000-0003-4820-876X"},"institutions":[{"id":"https://openalex.org/I129192303","display_name":"Universidade Federal do Rec\u00f4ncavo da Bahia","ror":"https://ror.org/057mvv518","country_code":"BR","type":"education","lineage":["https://openalex.org/I129192303"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Ramon Lopes","raw_affiliation_strings":["Universidade Federal do Rec\u00f4ncavo da Bahia, Cruz das Almas, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rec\u00f4ncavo da Bahia, Cruz das Almas, Brazil","institution_ids":["https://openalex.org/I129192303"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009808819","display_name":"Renato Assun\u00e7\u00e3o","orcid":"https://orcid.org/0000-0001-7442-9166"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Renato Assun\u00e7\u00e3o","raw_affiliation_strings":["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025385531","display_name":"Rodrygo L. T. Santos","orcid":"https://orcid.org/0000-0003-2921-8444"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrygo L.T. Santos","raw_affiliation_strings":["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101766026"],"corresponding_institution_ids":["https://openalex.org/I129192303"],"apc_list":null,"apc_paid":null,"fwci":4.6442,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.95102444,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"333","last_page":"340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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":0.9674000144004822,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9666000008583069,"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":0.7745404243469238},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6835603713989258},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6788402795791626},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6318560838699341},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5530041456222534},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5402742028236389},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.540228009223938},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5317975878715515},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4797033667564392},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.43595218658447266},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4072829782962799},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3722116947174072},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3227264881134033},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13184353709220886}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7745404243469238},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6835603713989258},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6788402795791626},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6318560838699341},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5530041456222534},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5402742028236389},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.540228009223938},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5317975878715515},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4797033667564392},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.43595218658447266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4072829782962799},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3722116947174072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3227264881134033},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13184353709220886},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2959100.2959132","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2959100.2959132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320322980","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","ror":"https://ror.org/00nc55f03"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W40476211","https://openalex.org/W46938732","https://openalex.org/W1854214752","https://openalex.org/W1965355809","https://openalex.org/W1991418309","https://openalex.org/W2006822005","https://openalex.org/W2009223819","https://openalex.org/W2023954349","https://openalex.org/W2044051625","https://openalex.org/W2069870183","https://openalex.org/W2101409192","https://openalex.org/W2123870574","https://openalex.org/W2133266261","https://openalex.org/W2138825227","https://openalex.org/W2140310134","https://openalex.org/W2155912844","https://openalex.org/W2161984370","https://openalex.org/W2168108570","https://openalex.org/W2196920274","https://openalex.org/W2219888463","https://openalex.org/W2531563875"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W2735929803","https://openalex.org/W4220714703","https://openalex.org/W1484355083","https://openalex.org/W3008845055","https://openalex.org/W2098758514","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622","https://openalex.org/W2556532874"],"abstract_inverted_index":{"Short-length":[0],"random":[1,33],"walks":[2],"on":[3,69,79],"the":[4,37,80,89,110,113],"bipartite":[5],"user-item":[6],"graph":[7],"have":[8],"recently":[9],"been":[10],"shown":[11],"to":[12,129],"provide":[13],"accurate":[14],"and":[15,25,102],"diverse":[16],"recommendations.":[17],"Nonetheless,":[18],"these":[19,45],"approaches":[20,46],"suffer":[21],"from":[22],"severe":[23],"time":[24],"space":[26],"requirements,":[27],"which":[28,50],"can":[29],"be":[30],"alleviated":[31],"via":[32],"walk":[34],"sampling,":[35],"at":[36],"cost":[38],"of":[39,88,112],"reduced":[40],"recommendation":[41],"quality.":[42],"In":[43,55],"addition,":[44],"ignore":[47],"users'":[48,90],"ratings,":[49],"further":[51],"limits":[52],"their":[53],"expressiveness.":[54],"this":[56],"paper,":[57],"we":[58,73],"introduce":[59],"a":[60],"computationally":[61],"efficient":[62],"graph-based":[63,101,131],"approach":[64],"for":[65],"collaborative":[66],"filtering":[67],"based":[68,78],"short-path":[70],"enumeration.":[71],"Moreover,":[72],"propose":[74],"three":[75],"scoring":[76],"functions":[77],"Bayesian":[81],"paradigm":[82],"that":[83],"effectively":[84],"exploit":[85],"distributional":[86],"aspects":[87],"ratings.":[91],"We":[92],"experiment":[93],"with":[94,116],"seven":[95],"publicly":[96],"available":[97],"datasets":[98],"against":[99],"state-of-the-art":[100],"matrix":[103],"factorization":[104],"approaches.":[105,132],"Our":[106],"empirical":[107],"results":[108,124],"demonstrate":[109,125],"effectiveness":[111],"proposed":[114],"approach,":[115],"significant":[117],"improvements":[118],"in":[119],"most":[120],"settings.":[121],"Furthermore,":[122],"analytical":[123],"its":[126],"efficiency":[127],"compared":[128],"other":[130]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}