{"id":"https://openalex.org/W4385541613","doi":"https://doi.org/10.1145/3539618.3592078","title":"Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation","display_name":"Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4385541613","doi":"https://doi.org/10.1145/3539618.3592078"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3592078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5089339915","display_name":"Taichi Liu","orcid":"https://orcid.org/0009-0007-0273-4648"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Taichi Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-0273-4648","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078622343","display_name":"Chen Gao","orcid":"https://orcid.org/0000-0002-7561-5646"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Gao","raw_affiliation_strings":["Tsinghua University & Huawei Noah's Ark Lab, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7561-5646","affiliations":[{"raw_affiliation_string":"Tsinghua University & Huawei Noah's Ark Lab, Beijing, China","institution_ids":["https://openalex.org/I2250955327","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120313172","display_name":"Zhenyu Wang","orcid":"https://orcid.org/0009-0001-9415-1859"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3701-8063","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028563023","display_name":"Dong Li","orcid":"https://orcid.org/0000-0002-8800-1483"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Li","raw_affiliation_strings":["Huawei Noah's Ark Lab, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8800-1483","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047509839","display_name":"Jianye Hao","orcid":"https://orcid.org/0000-0002-0422-8235"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianye Hao","raw_affiliation_strings":["Huawei Noah's Ark Lab, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0422-8235","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044100655","display_name":"Depeng Jin","orcid":"https://orcid.org/0000-0003-0419-5514"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Depeng Jin","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0419-5514","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355277","display_name":"Yong Li","orcid":"https://orcid.org/0000-0001-5617-1659"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5617-1659","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5089339915"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.0573,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.92635633,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2466","last_page":"2470"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9842000007629395,"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/cold-start","display_name":"Cold start (automotive)","score":0.922654390335083},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7729794979095459},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7647510766983032},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.7094386219978333},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6851711273193359},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.5773156881332397},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4667136073112488},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4175887405872345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39323416352272034}],"concepts":[{"id":"https://openalex.org/C2778956030","wikidata":"https://www.wikidata.org/wiki/Q5142477","display_name":"Cold start (automotive)","level":2,"score":0.922654390335083},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7729794979095459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7647510766983032},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7094386219978333},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6851711273193359},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.5773156881332397},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4667136073112488},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4175887405872345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39323416352272034},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3592078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G3188007771","display_name":null,"funder_award_id":"U20B2060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3734416573","display_name":null,"funder_award_id":"61972223","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4872662616","display_name":null,"funder_award_id":"U1936217","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8229519694","display_name":null,"funder_award_id":"2022YFB3104702","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G876154893","display_name":null,"funder_award_id":"2021TQ0027 and 2022M710006","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G8910773033","display_name":null,"funder_award_id":"2022YFB3104702","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2029507558","https://openalex.org/W2604433096","https://openalex.org/W2748058847","https://openalex.org/W2904362786","https://openalex.org/W2962756421","https://openalex.org/W3033630125","https://openalex.org/W3034348890","https://openalex.org/W3045200674","https://openalex.org/W3113410469","https://openalex.org/W3116172555","https://openalex.org/W3134330728","https://openalex.org/W3153108722","https://openalex.org/W3156939347","https://openalex.org/W3175865138","https://openalex.org/W3206310679","https://openalex.org/W3206458369","https://openalex.org/W3210286746","https://openalex.org/W4225110281","https://openalex.org/W4284681846","https://openalex.org/W4284889466","https://openalex.org/W4315977496","https://openalex.org/W6600213211"],"related_works":["https://openalex.org/W2497939785","https://openalex.org/W2219931199","https://openalex.org/W4241927574","https://openalex.org/W2735929803","https://openalex.org/W2971083348","https://openalex.org/W584290403","https://openalex.org/W3214288750","https://openalex.org/W2786642545","https://openalex.org/W3095646726","https://openalex.org/W2084560547"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Network":[2],"(GNN)-based":[3],"models":[4,32],"have":[5,151],"become":[6],"the":[7,14,21,35,49,89,93,129,142,155,159,163,171],"mainstream":[8],"approach":[9],"for":[10,26,92,112],"recommender":[11],"systems.":[12],"Despite":[13],"effectiveness,":[15],"they":[16],"are":[17,61,69,78],"still":[18,62],"suffering":[19],"from":[20,71,79],"cold-start":[22,36],"problem,":[23],"i.e.,":[24],"recommend":[25],"few-interaction":[27],"items.":[28,157],"Existing":[29],"GNN-based":[30],"recommendation":[31,90,115,164],"to":[33,140],"address":[34],"problem":[37],"mainly":[38],"focus":[39],"on":[40,121,182,194],"utilizing":[41],"auxiliary":[42],"features":[43],"of":[44,56,165,178,205],"users":[45],"and":[46,58,95,133,167,197],"items,":[47,199],"leaving":[48],"user-item":[50,122],"interactions":[51,149],"under-utilized.":[52],"However,":[53],"embeddings":[54,68,77],"distributions":[55],"cold":[57,66,94,143,166,198],"warm":[59,75,96,156,168,196],"items":[60,97,144,169],"largely":[63],"different,":[64],"since":[65],"items'":[67,76],"learned":[70],"lower-popularity":[72],"interactions,":[73],"while":[74],"higher-popularity":[80],"interactions.":[81,123],"Thus,":[82],"there":[83],"is":[84],"a":[85,107],"seesaw":[86],"phenomenon,":[87],"where":[88],"performance":[91,203],"cannot":[98],"be":[99],"improved":[100],"simultaneously.":[101],"To":[102],"this":[103,125],"end,":[104],"we":[105,127],"proposed":[106,160,188],"Uncertainty-aware":[108],"Consistency":[109],"learning":[110],"framework":[111,161],"Cold-start":[113],"item":[114],"(shorten":[116],"as":[117],"UCC)":[118],"solely":[119],"based":[120],"Under":[124],"framework,":[126],"train":[128],"teacher":[130],"model":[131,135],"(generator)":[132],"student":[134],"(recommender)":[136],"with":[137,145,154,200],"consistency":[138],"learning,":[139],"ensure":[141],"additionally":[146],"generated":[147],"low-uncertainty":[148],"can":[150],"similar":[152],"distribution":[153],"Therefore,":[158],"improves":[162],"at":[170],"same":[172],"time,":[173],"without":[174],"hurting":[175],"any":[176],"one":[177],"them.":[179],"Extensive":[180],"experiments":[181],"benchmark":[183],"datasets":[184],"demonstrate":[185],"that":[186],"our":[187],"method":[189],"significantly":[190],"outperforms":[191],"state-of-the-art":[192],"methods":[193],"both":[195],"an":[201],"average":[202],"improvement":[204],"27.6%.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-26T13:28:51.108037","created_date":"2025-10-10T00:00:00"}