{"id":"https://openalex.org/W4414359831","doi":"https://doi.org/10.24963/ijcai.2025/412","title":"Adversarial Propensity Weighting for Debiasing in Collaborative Filtering","display_name":"Adversarial Propensity Weighting for Debiasing in Collaborative Filtering","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359831","doi":"https://doi.org/10.24963/ijcai.2025/412"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/412","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102431173","display_name":"Kuiyu Zhu","orcid":"https://orcid.org/0009-0004-8261-9380"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuiyu Zhu","raw_affiliation_strings":["MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020025718","display_name":"Tao Qin","orcid":"https://orcid.org/0000-0002-9095-0776"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Qin","raw_affiliation_strings":["MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102845337","display_name":"Pinghui Wang","orcid":"https://orcid.org/0000-0002-1434-837X"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pinghui Wang","raw_affiliation_strings":["MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100328122","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0003-3663-5078"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3707","last_page":"3715"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.951200008392334,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.951200008392334,"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/debiasing","display_name":"Debiasing","score":0.9520999789237976},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.7103000283241272},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6955000162124634},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6610999703407288},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.6236000061035156},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.5902000069618225},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5587999820709229},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5501000285148621},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5464000105857849}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9520999789237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7337999939918518},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.7103000283241272},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6955000162124634},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6610999703407288},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.6236000061035156},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.5902000069618225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.579200029373169},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5501000285148621},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47519999742507935},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.44429999589920044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4117000102996826},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.3783000111579895},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C161664118","wikidata":"https://www.wikidata.org/wiki/Q1089933","display_name":"Churning","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.29679998755455017}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/412","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Debiased":[0],"recommendation":[1,12],"focuses":[2],"on":[3,11,23,40,50],"alleviating":[4],"the":[5,36,79,107,116,128,153],"negative":[6],"impact":[7],"of":[8,47,72,82,109],"various":[9,145],"biases":[10],"quality":[13],"to":[14,31,131],"achieve":[15],"fairer":[16],"personalized":[17],"recommendations.":[18],"Current":[19],"research":[20,39],"mainly":[21],"relies":[22],"propensity":[24,65,100,113,118],"score":[25,66],"estimation":[26],"or":[27],"causal":[28,51],"inference":[29],"methods":[30,48,58,143],"alleviate":[32],"selection":[33],"bias;":[34],"at":[35],"same":[37],"time,":[38],"prevalence":[41],"bias":[42,68,75,125],"has":[43],"proposed":[44],"a":[45,92],"variety":[46],"based":[49],"graphs":[52],"and":[53,70,74,102,120,124,159,164],"contrastive":[54],"learning.":[55,104],"However,":[56],"these":[57],"have":[59],"shortcomings":[60],"in":[61,112],"dealing":[62],"with":[63,152],"unstable":[64],"estimates,":[67],"interactions,":[69],"decoupling":[71],"interest":[73],"signals,":[76],"which":[77],"limits":[78],"performance":[80],"improvement":[81],"recommender":[83],"systems.":[84],"To":[85],"this":[86,88],"end,":[87],"paper":[89],"proposes":[90],"APWCF,":[91],"collaborative":[93],"filtering":[94],"debiased":[95],"method":[96],"that":[97,138],"combines":[98],"dynamic":[99,117],"modeling":[101],"adversarial":[103,129],"APWCF":[105,139],"solves":[106],"problem":[108],"high":[110],"variance":[111],"scores":[114],"through":[115,127],"factor,":[119],"decouples":[121],"user":[122],"interests":[123],"signals":[126],"learning":[130],"effectively":[132],"remove":[133],"multiple":[134],"biases.":[135],"Experiments":[136],"show":[137],"significantly":[140],"outperforms":[141],"existing":[142],"across":[144],"benchmark":[146],"datasets":[147],"from":[148],"different":[149],"domains.":[150],"Compared":[151],"current":[154],"optimal":[155],"baseline":[156],"PDA,":[157],"Recall@10":[158],"NDCG@10":[160],"improve":[161],"by":[162],"0.10%-5.42%":[163],"1.01%-8.60%":[165],"respectively.":[166]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
