{"id":"https://openalex.org/W4306317044","doi":"https://doi.org/10.1145/3511808.3557341","title":"A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation","display_name":"A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317044","doi":"https://doi.org/10.1145/3511808.3557341"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557341","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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/A5082763858","display_name":"Yuexin Wu","orcid":"https://orcid.org/0000-0001-9005-5678"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuexin Wu","raw_affiliation_strings":["University of Memphis, Memphis, TN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis, Memphis, TN, USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027237221","display_name":"Xiaolei Huang","orcid":"https://orcid.org/0000-0003-0478-8715"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolei Huang","raw_affiliation_strings":["University of Memphis, Memphis, TN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis, Memphis, TN, USA","institution_ids":["https://openalex.org/I94658018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94658018"],"apc_list":null,"apc_paid":null,"fwci":0.5823,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67867353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2199","last_page":"2209"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9735000133514404,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9677000045776367,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gumbel-distribution","display_name":"Gumbel distribution","score":0.8464601039886475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6446678042411804},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5967780947685242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5712674856185913},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.49863290786743164},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4816350042819977},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40992236137390137},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22003835439682007},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19187021255493164},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.09731724858283997}],"concepts":[{"id":"https://openalex.org/C137610916","wikidata":"https://www.wikidata.org/wiki/Q1096862","display_name":"Gumbel distribution","level":3,"score":0.8464601039886475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6446678042411804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5967780947685242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5712674856185913},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.49863290786743164},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4816350042819977},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40992236137390137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22003835439682007},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19187021255493164},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.09731724858283997}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557341","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2023639956","https://openalex.org/W2054141820","https://openalex.org/W2069870183","https://openalex.org/W2143017621","https://openalex.org/W2250539671","https://openalex.org/W2520348554","https://openalex.org/W2605350416","https://openalex.org/W2747329762","https://openalex.org/W2788376297","https://openalex.org/W2884561390","https://openalex.org/W2897660518","https://openalex.org/W2913592748","https://openalex.org/W2947936100","https://openalex.org/W2949655105","https://openalex.org/W2962986764","https://openalex.org/W2963367478","https://openalex.org/W2963691377","https://openalex.org/W2963703197","https://openalex.org/W2997701788","https://openalex.org/W2998123743","https://openalex.org/W3080236009","https://openalex.org/W3094575529","https://openalex.org/W3147409145","https://openalex.org/W3152972811","https://openalex.org/W3167920080","https://openalex.org/W3210990451","https://openalex.org/W4212774754","https://openalex.org/W4282934351","https://openalex.org/W4288770410","https://openalex.org/W4297971002"],"related_works":["https://openalex.org/W2115040659","https://openalex.org/W2392757156","https://openalex.org/W3121924949","https://openalex.org/W2951988075","https://openalex.org/W2270643620","https://openalex.org/W1570428685","https://openalex.org/W2083778309","https://openalex.org/W1498453022","https://openalex.org/W1982454386","https://openalex.org/W4210771670"],"abstract_inverted_index":{"Rating":[0],"prediction":[1,42],"is":[2,199],"a":[3,55,58,71,92,106,129],"core":[4],"problem":[5],"in":[6,20,39],"recommender":[7],"systems":[8],"to":[9,30,47,65,78,96,111,133],"quantify":[10],"users'":[11],"preferences":[12],"towards":[13],"items.":[14],"However,":[15],"rating":[16,41,80,121,136],"imbalance":[17,81],"naturally":[18],"roots":[19],"real-world":[21],"user":[22,124],"ratings":[23],"that":[24,159],"cause":[25],"biased":[26,173],"predictions":[27,174],"and":[28,60,82,117,123,148,154,170,185,187],"lead":[29],"poor":[31],"performance":[32,166],"on":[33,142,152,194],"tail":[34,176],"ratings.":[35,177],"While":[36],"existing":[37],"approaches":[38,52],"the":[40,66,87,120,160,168,172,189],"task":[43],"deploy":[44,105],"weighted":[45],"cross-entropy":[46],"re-weight":[48],"training":[49],"samples,":[50],"such":[51],"commonly":[53],"assume":[54],"normal":[56,67,184],"distribution,":[57],"symmetrical":[59],"balanced":[61],"space.":[62,102],"In":[63],"contrast":[64],"assumption,":[68],"we":[69,104,127],"propose":[70,91],"novel":[72],"Gumbel-based":[73,93,192],"Variational":[74],"Network":[75],"framework":[76],"(GVN)":[77],"model":[79],"augment":[83],"feature":[84],"representations":[85],"by":[86],"Gumbel":[88],"distributions.":[89],"We":[90,138,178],"variational":[94],"encoder":[95],"transform":[97],"features":[98],"into":[99],"non-normal":[100],"vector":[101],"Second,":[103],"multi-scale":[107],"convolutional":[108],"fusion":[109],"network":[110],"integrate":[112],"comprehensive":[113],"views":[114],"of":[115,175,191],"users":[116],"items":[118],"from":[119],"matrix":[122],"reviews.":[125],"Third,":[126],"adopt":[128],"skip":[130],"connection":[131],"module":[132],"personalize":[134],"final":[135],"predictions.":[137],"conduct":[139],"extensive":[140],"experiments":[141],"five":[143],"datasets":[144,169],"with":[145,180],"both":[146],"errors-":[147],"ranking-based":[149],"metrics.":[150],"Experiments":[151],"ranking":[153],"regression":[155],"evaluation":[156],"tasks":[157],"prove":[158],"GVN":[161],"can":[162],"effectively":[163],"achieve":[164],"state-of-the-art":[165],"across":[167],"reduce":[171],"compare":[179],"various":[181],"distributions":[182],"(e.g.,":[183],"Poisson)":[186],"demonstrate":[188],"effectiveness":[190],"methods":[193],"class-imbalance":[195],"modeling.":[196],"The":[197],"code":[198],"available":[200],"at":[201],"https://github.com/woqingdoua/Gumbel-recommendation-for-imbalanced-data.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
