{"id":"https://openalex.org/W4384901164","doi":"https://doi.org/10.1145/3539618.3592055","title":"Robust Causal Inference for Recommender System to Overcome Noisy Confounders","display_name":"Robust Causal Inference for Recommender System to Overcome Noisy Confounders","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384901164","doi":"https://doi.org/10.1145/3539618.3592055"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3592055","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592055","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/A5060066118","display_name":"Zhiheng Zhang","orcid":"https://orcid.org/0000-0001-6767-7487"},"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":"Zhiheng Zhang","raw_affiliation_strings":["Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6767-7487","affiliations":[{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048783161","display_name":"Quanyu Dai","orcid":"https://orcid.org/0000-0001-7578-2738"},"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":"Quanyu Dai","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-7578-2738","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101755392","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0003-0144-1775"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Chen","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0144-1775","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Shenzhen, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021124418","display_name":"Zhenhua Dong","orcid":"https://orcid.org/0000-0002-2231-4663"},"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":"Zhenhua Dong","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-2231-4663","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054330014","display_name":"Ruiming Tang","orcid":"https://orcid.org/0000-0002-9224-2431"},"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":"Ruiming Tang","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9224-2431","affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5060066118"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.6699,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7491501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2349","last_page":"2353"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9980000257492065,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9975000023841858,"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":0.7486153244972229},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7479265928268433},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.727785587310791},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7086113095283508},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6832304000854492},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6173614859580994},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5871177911758423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4965692162513733},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4643924832344055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4593757390975952},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4027363955974579},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.23066377639770508},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1795358955860138},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1531059443950653}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7486153244972229},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7479265928268433},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.727785587310791},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7086113095283508},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6832304000854492},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6173614859580994},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5871177911758423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4965692162513733},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4643924832344055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4593757390975952},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4027363955974579},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.23066377639770508},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1795358955860138},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1531059443950653},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3592055","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592055","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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1490576214","https://openalex.org/W2036038497","https://openalex.org/W2132917208","https://openalex.org/W2137370054","https://openalex.org/W2167032986","https://openalex.org/W2912240430","https://openalex.org/W2950416834","https://openalex.org/W2962907114","https://openalex.org/W3012576969","https://openalex.org/W3083370850","https://openalex.org/W3089238887","https://openalex.org/W3153906321","https://openalex.org/W3164238513","https://openalex.org/W4290927708","https://openalex.org/W4290944505","https://openalex.org/W4382240101"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Recently,":[0],"there":[1],"has":[2],"been":[3,44],"growing":[4],"interest":[5],"in":[6,66],"integrating":[7],"causal":[8],"inference":[9],"into":[10],"recommender":[11],"systems":[12],"to":[13,46,87,123,144],"answer":[14],"the":[15,21,64,99,102,108,113,117,133],"hypothetical":[16],"question:":[17],"\"what":[18],"would":[19],"be":[20],"potential":[22],"feedback":[23],"when":[24],"a":[25,29,75,95,129],"user":[26],"is":[27,61],"recommended":[28],"product?\"":[30],"Various":[31],"unbiased":[32],"estimators,":[33],"including":[34],"Inverse":[35],"Propensity":[36],"Score":[37],"(IPS)":[38],"and":[39,110,116,138,151],"Doubly":[40],"Robust":[41],"(DR),":[42],"have":[43],"proposed":[45,92],"address":[47,70],"this":[48,71],"question.":[49],"However,":[50],"these":[51],"estimators":[52,147],"often":[53],"assume":[54],"that":[55],"confounders":[56],"are":[57],"precisely":[58],"observable,":[59],"which":[60,83],"not":[62],"always":[63],"case":[65],"real-world":[67,150],"scenarios.":[68],"To":[69],"challenge,":[72],"we":[73],"propose":[74],"novel":[76],"method":[77,93],"called":[78],"Adversarial":[79],"Training-based":[80],"IPS":[81],"(AT-IPS),":[82],"uses":[84],"adversarial":[85],"training":[86],"handle":[88],"noisy":[89],"confounders.":[90],"The":[91],"defines":[94],"feasible":[96],"region":[97],"for":[98],"confounders,":[100],"obtains":[101],"worst-case":[103],"noise":[104,122],"(adversarial":[105],"noise)":[106],"within":[107],"region,":[109],"jointly":[111],"trains":[112],"propensity":[114],"model":[115,119],"prediction":[118],"against":[120],"such":[121],"improve":[124],"their":[125],"robustness.":[126],"We":[127],"provide":[128],"theoretical":[130],"analysis":[131],"of":[132,136],"accuracy-robustness":[134],"tradeoff":[135],"AT-IPS":[137],"demonstrate":[139],"its":[140],"superior":[141],"performance":[142],"compared":[143],"other":[145],"popular":[146],"on":[148],"both":[149],"semi-synthetic":[152],"datasets.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-11T08:15:01.531666","created_date":"2025-10-10T00:00:00"}