{"id":"https://openalex.org/W2211270632","doi":null,"title":"Sparse probabilistic matrix factorization by Llaplace distribution for collaborative filtering","display_name":"Sparse probabilistic matrix factorization by Llaplace distribution for collaborative filtering","publication_year":2015,"publication_date":"2015-07-25","ids":{"openalex":"https://openalex.org/W2211270632","mag":"2211270632"},"language":"en","primary_location":{"id":"mag:2211270632","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ijcai/ijcai2015.html#JingWY15","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International 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 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/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407","observed_orcids":["https://orcid.org/0000-0001-7578-3407","https://orcid.org/0000-0003-2160-1721"]},"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":"https://orcid.org/0000-0001-7578-3407","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/A5100396101","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-9844-0615","observed_orcids":["https://orcid.org/0000-0002-9844-0615"]},"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":"Peng Wang","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/A5101420430","display_name":"Liu Yang","orcid":"https://orcid.org/0000-0001-6145-0978","observed_orcids":["https://orcid.org/0000-0001-6145-0978"]},"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":"Liu Yang","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China"],"raw_orcid":"https://orcid.org/0000-0001-6145-0978","affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.9087,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92385587,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1771","last_page":"1777"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0,"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"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"collaborative filtering","score":0.8989999890327454},{"id":"https://openalex.org/keywords/laplacian-distribution","display_name":"Laplacian distribution","score":0.8799999952316284},{"id":"https://openalex.org/keywords/sparse-rating-data","display_name":"sparse rating data","score":0.5720000267028809},{"id":"https://openalex.org/keywords/long-tail-items","display_name":"long-tail items","score":0.8169999718666077},{"id":"https://openalex.org/keywords/latent-features","display_name":"latent features","score":0.6589999794960022},{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.6840000152587891}],"concepts":[{"id":"https://openalex.org/C2776156558","wikidata":"https://www.wikidata.org/wiki/Q4353746","display_name":"MovieLens","level":4,"score":0.8861923217773438},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.805955171585083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6613826751708984},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6218289136886597},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5940520763397217},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.591778039932251},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5844244956970215},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.578001856803894},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.5445480942726135},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.5040630102157593},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4206061065196991},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.373490571975708},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3675229847431183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.348499596118927},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2890984117984772},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.20660123229026794},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.14577260613441467},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2211270632","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/ijcai/ijcai2015.html#JingWY15","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International 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 Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W45374770","https://openalex.org/W64171066","https://openalex.org/W195465510","https://openalex.org/W199797433","https://openalex.org/W1982114217","https://openalex.org/W2043099794","https://openalex.org/W2085040216","https://openalex.org/W2108572844","https://openalex.org/W2131555991","https://openalex.org/W2137245235","https://openalex.org/W2138311613","https://openalex.org/W2138899136","https://openalex.org/W2146682077","https://openalex.org/W2148507806","https://openalex.org/W2157232038","https://openalex.org/W2171960770","https://openalex.org/W2188214461","https://openalex.org/W2497431972"],"related_works":["https://openalex.org/W1962876118","https://openalex.org/W2978890944","https://openalex.org/W2137245235","https://openalex.org/W3006414372","https://openalex.org/W2151464835","https://openalex.org/W2144302328","https://openalex.org/W3120726628","https://openalex.org/W1982487445","https://openalex.org/W2788017153","https://openalex.org/W2335654053"],"abstract_inverted_index":{"In":[0,79],"recommendation":[1,193],"systems,":[2],"probabilistic":[3,86,161],"matrix":[4,87],"factorization":[5,88],"(PMF)":[6],"is":[7,111,138,153],"a":[8,71,84,93,148],"state-of-the-art":[9],"collaborative":[10],"filtering":[11],"method":[12,89],"by":[13,91],"determining":[14],"the":[15,28,33,42,45,98,117,129,144,158,180,192],"latent":[16,58,121],"features":[17,59,122],"to":[18,41,96,106,115,125,142,155,175],"represent":[19],"users":[20],"and":[21,36,119,169,183],"items.":[22,146,196],"However,":[23],"two":[24],"major":[25],"issues":[26],"limiting":[27],"usefulness":[29],"of":[30,57,74,165,194],"PMF":[31,182,188],"are":[32,49,60,134],"sparsity":[34],"problem":[35],"long-tail":[37],"distribution.":[38],"Sparsity":[39],"refers":[40],"situation":[43],"that":[44,54,70,177],"observed":[46],"rating":[47],"data":[48],"sparse,":[50],"which":[51,110,137],"results":[52],"in":[53,131],"only":[55],"part":[56],"informative":[61],"for":[62,113,140,191],"describing":[63],"each":[64,126],"item/user.":[65,127],"Long":[66],"tail":[67,145,195],"distribution":[68,95,103,133],"implies":[69],"large":[72],"fraction":[73],"items":[75],"have":[76,172],"few":[77],"ratings.":[78],"this":[80],"work,":[81],"we":[82],"propose":[83],"sparse":[85,108,160],"(SPMF)":[90],"utilizing":[92],"Laplacian":[94,102,132],"model":[97],"item/user":[99],"factor":[100],"vector.":[101],"has":[104],"ability":[105],"generate":[107],"coding,":[109],"beneficial":[112],"SPMF":[114,141,178],"distinguish":[116],"relevant":[118],"irrelevant":[120],"with":[123],"respect":[124],"Meanwhile,":[128],"tails":[130],"comparatively":[135],"heavy,":[136],"rewarding":[139],"recommend":[143],"Furthermore,":[147],"distributed":[149],"Gibbs":[150],"sampling":[151],"algorithm":[152],"developed":[154],"efficiently":[156],"train":[157],"proposed":[159],"model.":[162],"A":[163],"series":[164],"experiments":[166],"on":[167],"Netflix":[168],"Movielens":[170],"datasets":[171],"been":[173],"conducted":[174],"demonstrate":[176],"outperforms":[179],"existing":[181],"its":[184],"extended":[185],"version":[186],"Bayesian":[187],"(BPMF),":[189],"especially":[190]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}