{"id":"https://openalex.org/W4410636559","doi":"https://doi.org/10.1145/3701716.3715475","title":"Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods","display_name":"Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4410636559","doi":"https://doi.org/10.1145/3701716.3715475"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3715475","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715475","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715475","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715475","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087551074","display_name":"Meng Yan","orcid":"https://orcid.org/0000-0001-8478-4823"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Meng Yan","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-8478-4823","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007403499","display_name":"Cai Xu","orcid":"https://orcid.org/0000-0002-7191-7348"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cai Xu","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-7191-7348","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018002127","display_name":"Xujing Wang","orcid":"https://orcid.org/0009-0003-5336-8845"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xujing Wang","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0009-0003-5336-8845","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065194313","display_name":"Ziyu Guan","orcid":"https://orcid.org/0000-0003-2413-4698"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Guan","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2413-4698","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066877556","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0002-9767-1323"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-9767-1323","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yuhang Zhou","orcid":"https://orcid.org/0009-0000-4460-2001"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Zhou","raw_affiliation_strings":["Communication University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-4460-2001","affiliations":[{"raw_affiliation_string":"Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5087551074"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":2.8622,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90153677,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1460","last_page":"1464"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9819999933242798,"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":0.6847251653671265},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5133060812950134},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.4800330698490143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3929961621761322},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3510240316390991},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2634354531764984},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15034636855125427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6847251653671265},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5133060812950134},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.4800330698490143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3929961621761322},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3510240316390991},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2634354531764984},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15034636855125427}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3701716.3715475","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715475","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715475","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3701716.3715475","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3715475","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3715475","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6499999761581421,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1064793343","display_name":null,"funder_award_id":"62472340","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3326707821","display_name":null,"funder_award_id":"62425605","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4998866454","display_name":null,"funder_award_id":"2024CY2-GJHX-15","funder_id":"https://openalex.org/F4320336350","funder_display_name":"Key Research and Development Projects of Shaanxi Province"},{"id":"https://openalex.org/G5655994340","display_name":null,"funder_award_id":"62303366","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7041986801","display_name":null,"funder_award_id":"62133012, 62472340, 62425605, 62303366","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"},{"id":"https://openalex.org/G8746616028","display_name":null,"funder_award_id":"62133012","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/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320336350","display_name":"Key Research and Development Projects of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410636559.pdf","grobid_xml":"https://content.openalex.org/works/W4410636559.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2945623882","https://openalex.org/W3031918285","https://openalex.org/W3045200674","https://openalex.org/W3098138727","https://openalex.org/W3201888171","https://openalex.org/W4321380694","https://openalex.org/W4324054429","https://openalex.org/W4379744065","https://openalex.org/W4385566358","https://openalex.org/W4385567729","https://openalex.org/W4388572337","https://openalex.org/W4393156544","https://openalex.org/W4394744523","https://openalex.org/W4402029651","https://openalex.org/W4402697128"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Recommender":[0],"systems":[1],"based":[2,73,150],"on":[3,74,115,151,174],"graph":[4],"neural":[5],"networks":[6],"perform":[7],"well":[8],"in":[9,17,94,182],"tasks":[10],"such":[11,22],"as":[12,23,65],"rating":[13,139],"and":[14,26,68,126,168,185],"ranking.":[15],"However,":[16],"real-world":[18],"recommendation":[19,107,171,186],"scenarios,":[20],"noise":[21,46],"user":[24,152],"misuse":[25],"malicious":[27],"advertisement":[28],"gradually":[29],"accumulates":[30],"through":[31],"the":[32,45,49,53,59,69,75,88,91,103,116,128,163,178],"message":[33],"propagation":[34,47],"mechanism.":[35],"Even":[36],"if":[37],"existing":[38,104],"studies":[39],"mitigate":[40,148],"their":[41],"effects":[42],"by":[43],"reducing":[44],"weights,":[48],"severe":[50],"sparsity":[51],"of":[52,90,102,131,165,180],"recommender":[54],"system":[55],"still":[56],"leads":[57],"to":[58,86,124,147,161],"low-weighted":[60],"noisy":[61,97],"neighbors":[62],"being":[63],"mistaken":[64],"meaningful":[66],"information,":[67],"prediction":[70,92,129,183],"result":[71],"obtained":[72],"polluted":[76],"nodes":[77],"is":[78,84],"not":[79],"entirely":[80],"trustworthy.":[81],"Therefore,":[82],"it":[83,110],"crucial":[85],"measure":[87],"confidence":[89,130,158,184],"results":[93],"this":[95],"highly":[96],"framework.":[98],"Furthermore,":[99],"our":[100],"evaluation":[101],"representative":[105],"GNN-based":[106,132],"shows":[108],"that":[109,142],"suffers":[111],"from":[112],"overconfidence.":[113],"Based":[114],"above":[117],"considerations,":[118],"we":[119,136],"propose":[120,137],"a":[121,138,157],"new":[122],"method":[123,141],"quantify":[125],"calibrate":[127],"recommendations":[133],"(Conf-GNNRec).":[134],"Specifically,":[135],"calibration":[140],"dynamically":[143],"adjusts":[144],"excessive":[145],"ratings":[146],"overconfidence":[149,164],"personalization.":[153],"We":[154],"also":[155],"design":[156],"loss":[159],"function":[160],"reduce":[162],"negative":[166],"samples":[167],"effectively":[169],"improve":[170],"performance.":[172,187],"Experiments":[173],"public":[175],"datasets":[176],"demonstrate":[177],"validity":[179],"Conf-GNNRec":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2025-10-10T00:00:00"}