{"id":"https://openalex.org/W4399321650","doi":"https://doi.org/10.1007/s41109-024-00630-6","title":"Augmented degree correction for bipartite networks with applications to recommender systems","display_name":"Augmented degree correction for bipartite networks with applications to recommender systems","publication_year":2024,"publication_date":"2024-06-04","ids":{"openalex":"https://openalex.org/W4399321650","doi":"https://doi.org/10.1007/s41109-024-00630-6"},"language":"en","primary_location":{"id":"doi:10.1007/s41109-024-00630-6","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s41109-024-00630-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41109-024-00630-6.pdf","source":{"id":"https://openalex.org/S3035517252","display_name":"Applied Network Science","issn_l":"2364-8228","issn":["2364-8228"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Network Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s41109-024-00630-6.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087091360","display_name":"Benjamin Leinwand","orcid":"https://orcid.org/0000-0002-7187-6961"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Benjamin Leinwand","raw_affiliation_strings":["Department of Mathematical Sciences, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004623767","display_name":"Vladas Pipiras","orcid":"https://orcid.org/0000-0001-6838-6294"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vladas Pipiras","raw_affiliation_strings":["Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Hanes Hall, Chapel Hill, NC, 27599, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Hanes Hall, Chapel Hill, NC, 27599, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087091360"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":{"value":790,"currency":"GBP","value_usd":969},"apc_paid":{"value":790,"currency":"GBP","value_usd":969},"fwci":0.3399,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51710672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.8454067707061768},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.8209988474845886},{"id":"https://openalex.org/keywords/degree","display_name":"Degree (music)","score":0.7660028338432312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6202566623687744},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3269132375717163},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2811015844345093},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.058802127838134766},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05498337745666504}],"concepts":[{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.8454067707061768},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.8209988474845886},{"id":"https://openalex.org/C2775997480","wikidata":"https://www.wikidata.org/wiki/Q586277","display_name":"Degree (music)","level":2,"score":0.7660028338432312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6202566623687744},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3269132375717163},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2811015844345093},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.058802127838134766},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05498337745666504},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s41109-024-00630-6","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s41109-024-00630-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41109-024-00630-6.pdf","source":{"id":"https://openalex.org/S3035517252","display_name":"Applied Network Science","issn_l":"2364-8228","issn":["2364-8228"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Network Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:895490ca819f4687859fa6517a67880c","is_oa":false,"landing_page_url":"https://doaj.org/article/895490ca819f4687859fa6517a67880c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Network Science, Vol 9, Iss 1, Pp 1-27 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s41109-024-00630-6","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1007/s41109-024-00630-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41109-024-00630-6.pdf","source":{"id":"https://openalex.org/S3035517252","display_name":"Applied Network Science","issn_l":"2364-8228","issn":["2364-8228"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Network Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2183535435","display_name":null,"funder_award_id":"DMS-2113662","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2343957700","display_name":null,"funder_award_id":"2113662","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4133067109","display_name":null,"funder_award_id":"2134107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8004330269","display_name":null,"funder_award_id":"DMS-2134107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399321650.pdf","grobid_xml":"https://content.openalex.org/works/W4399321650.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2037594831","https://openalex.org/W2117354486","https://openalex.org/W2134332047","https://openalex.org/W2604314403","https://openalex.org/W2605350416","https://openalex.org/W2611328865","https://openalex.org/W2957946950","https://openalex.org/W3152893301","https://openalex.org/W3165774164","https://openalex.org/W4206314707"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2371352078","https://openalex.org/W2953461625","https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2077383796","https://openalex.org/W2080136900","https://openalex.org/W2150182025","https://openalex.org/W3092950680"],"abstract_inverted_index":{"Abstract":[0],"In":[1,45],"recommender":[2],"systems,":[3],"users":[4,20,41,96,119],"rate":[5,22],"items,":[6,29],"and":[7,42,83,97,101,120,139,141],"are":[8],"subsequently":[9],"served":[10],"other":[11,213],"product":[12],"recommendations":[13],"based":[14],"on":[15,131,168,176],"these":[16],"ratings.":[17],"Even":[18],"though":[19,212],"usually":[21],"a":[23,62,169,177],"tiny":[24],"percentage":[25],"of":[26,64,87,106,118,123,161,180,192,239],"the":[27,30,50,84,88,104,143,147,154,159,195,224,235],"available":[28],"system":[31],"tries":[32],"to":[33,78,115,165,230],"estimate":[34],"unobserved":[35,81],"preferences":[36],"by":[37,112],"finding":[38],"similarities":[39],"across":[40,43],"items.":[44],"this":[46],"work,":[47],"we":[48,69],"treat":[49],"observed":[51],"ratings":[52,107,197],"data":[53,171,178,198,220],"as":[54,173,175],"partially":[55],"observed,":[56],"dense,":[57,75],"weighted,":[58],"bipartite":[59,85],"networks.":[60],"For":[61],"class":[63],"systems":[65],"without":[66],"outside":[67],"information,":[68],"adapt":[70],"an":[71],"approach":[72,91,164],"developed":[73],"for":[74,80,136],"weighted":[76],"networks":[77],"account":[79],"edges":[82],"nature":[86],"problem.":[89],"The":[90,126],"begins":[92],"with":[93],"clustering":[94],"both":[95,187],"items":[98,124],"into":[99],"communities,":[100],"locally":[102],"estimates":[103],"patterns":[105,243],"within":[108],"each":[109],"subnetwork":[110],"induced":[111],"restricting":[113],"attention":[114],"one":[116,121],"community":[117,122],"community.":[125],"local":[127,133],"fitting":[128],"procedure":[129],"relies":[130],"estimating":[132],"sociability":[134],"parameters":[135],"every":[137],"user":[138],"item,":[140],"selecting":[142],"function":[144],"that":[145,227],"determines":[146],"degree":[148],"correction":[149],"contours":[150],"which":[151],"best":[152],"models":[153],"underlying":[155],"data.":[156],"We":[157],"compare":[158],"performance":[160,185],"our":[162,200],"proposed":[163,201,236],"existing":[166,206],"methods":[167],"simulated":[170],"set,":[172,199],"well":[174],"set":[179],"joke":[181,196],"ratings,":[182],"examining":[183],"model":[184,202],"in":[186,208,248],"cases":[188],"at":[189],"differing":[190],"levels":[191],"sparsity.":[193],"On":[194],"performs":[203],"better":[204,216],"than":[205],"alternatives":[207],"relatively":[209],"sparse":[210],"settings,":[211],"approaches":[214],"achieve":[215],"results":[217,225],"when":[218],"more":[219],"is":[221,254],"available.":[222],"Collectively,":[223],"indicate":[226],"despite":[228],"struggling":[229],"pick":[231],"up":[232],"subtler":[233],"signals,":[234],"approach\u2019s":[237],"recovery":[238],"large":[240],"scale,":[241],"coarse":[242],"may":[244],"still":[245],"be":[246],"useful":[247],"practical":[249],"settings":[250],"where":[251],"high":[252],"sparsity":[253],"typical.":[255]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}