{"id":"https://openalex.org/W4400524681","doi":"https://doi.org/10.1145/3626772.3657772","title":"Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation","display_name":"Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4400524681","doi":"https://doi.org/10.1145/3626772.3657772"},"language":"en","primary_location":{"id":"doi:10.1145/3626772.3657772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657772","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3626772.3657772","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071089113","display_name":"Dan Luo","orcid":"https://orcid.org/0000-0003-3243-2441"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dan Luo","raw_affiliation_strings":["Lehigh University, Bethlehem, PA, USA"],"affiliations":[{"raw_affiliation_string":"Lehigh University, Bethlehem, PA, USA","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089307887","display_name":"Lixin Zou","orcid":"https://orcid.org/0000-0001-6755-871X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixin Zou","raw_affiliation_strings":["Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089655391","display_name":"Qingyao Ai","orcid":"https://orcid.org/0000-0002-5030-709X"},"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":"Qingyao Ai","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059330077","display_name":"Zhiyu Chen","orcid":"https://orcid.org/0000-0002-3096-7912"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiyu Chen","raw_affiliation_strings":["Amazon.com, Inc., Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com, Inc., Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734069","display_name":"Chenliang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenliang Li","raw_affiliation_strings":["Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054482111","display_name":"Dawei Yin","orcid":"https://orcid.org/0000-0002-8846-2001"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Yin","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042328810","display_name":"Brian D. Davison","orcid":"https://orcid.org/0000-0002-9326-3648"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian D. Davison","raw_affiliation_strings":["Lehigh University, Bethlehem, PA, USA"],"affiliations":[{"raw_affiliation_string":"Lehigh University, Bethlehem, PA, USA","institution_ids":["https://openalex.org/I186143895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5071089113"],"corresponding_institution_ids":["https://openalex.org/I186143895"],"apc_list":null,"apc_paid":null,"fwci":0.695,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.74354251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1535","last_page":"1545"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9987999796867371,"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":0.9987999796867371,"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/T11269","display_name":"Algorithms and Data Compression","score":0.998199999332428,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9954000115394592,"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/rank","display_name":"Rank (graph theory)","score":0.6521449089050293},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6148333549499512},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5530263185501099},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41010046005249023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2247818112373352},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07287967205047607},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.06917491555213928}],"concepts":[{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6521449089050293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6148333549499512},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5530263185501099},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41010046005249023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2247818112373352},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07287967205047607},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.06917491555213928},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626772.3657772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657772","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3626772.3657772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626772.3657772","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1974360117","https://openalex.org/W1990190154","https://openalex.org/W1992549066","https://openalex.org/W2035720976","https://openalex.org/W2047221353","https://openalex.org/W2069870183","https://openalex.org/W2090883204","https://openalex.org/W2113640060","https://openalex.org/W2152314154","https://openalex.org/W2295476135","https://openalex.org/W2340526403","https://openalex.org/W2470088391","https://openalex.org/W2507134384","https://openalex.org/W2769473018","https://openalex.org/W2975880037","https://openalex.org/W2989067042","https://openalex.org/W2999905431","https://openalex.org/W3048742138","https://openalex.org/W3072481648","https://openalex.org/W3102216552","https://openalex.org/W3106212003","https://openalex.org/W3115087172","https://openalex.org/W3130740428","https://openalex.org/W3155345376","https://openalex.org/W3170841641","https://openalex.org/W3194610749","https://openalex.org/W3199916614","https://openalex.org/W3210951392","https://openalex.org/W4284672561","https://openalex.org/W4284675352","https://openalex.org/W4284975772","https://openalex.org/W4288079518","https://openalex.org/W4290878292","https://openalex.org/W4292217701","https://openalex.org/W4306316890","https://openalex.org/W4306316957","https://openalex.org/W4366559947","https://openalex.org/W4385562516","https://openalex.org/W6600050674","https://openalex.org/W6600339457","https://openalex.org/W6657872126","https://openalex.org/W6839314779"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0],"logs":[1],"of":[2,5,97,103,108,122,129,140,151,208,272],"the":[3,53,57,95,106,109,115,119,127,167,202,206,234,270,273],"use":[4,128,156],"a":[6,14,31,89,98,130,137,147,176,216,221,227,245],"search":[7,154,171],"engine":[8],"provide":[9,68],"sufficient":[10],"data":[11],"to":[12,43,67,146,162,188,204],"train":[13],"better":[15],"ranker.":[16,117,248],"However,":[17,79],"it":[18,92],"is":[19,88,201,264],"well":[20],"known":[21],"that":[22,34,56,85,135,239,261],"such":[23],"implicit":[24,168],"feedback":[25],"reflects":[26],"biases,":[27],"and":[28,71,75,105,160,181,244,259,267],"in":[29,170,175,192],"particular":[30],"presentation":[32],"bias":[33,50,58],"favors":[35],"higher-ranked":[36],"results.":[37],"Unbiased":[38],"Learning-to-Rank":[39],"(ULTR)":[40],"methods":[41,63,82,125,187],"attempt":[42],"optimize":[44],"performance":[45,74,120],"by":[46,114,211],"jointly":[47,241],"modeling":[48],"this":[49],"along":[51],"with":[52],"ranker":[54,132],"so":[55],"can":[59,240],"be":[60],"removed.":[61],"Such":[62],"have":[64],"been":[65],"shown":[66],"theoretical":[69],"soundness,":[70],"promise":[72],"superior":[73],"low":[76],"deployment":[77],"costs.":[78],"existing":[80,123,185],"ULTR":[81,124,186,212],"don't":[83],"recognize":[84],"query-document":[86],"relevance":[87,104,145],"confounder":[90],"--":[91,133],"affects":[93],"both":[94],"likelihood":[96,107],"result":[99,110],"being":[100,111],"clicked":[101],"because":[102],"ranked":[112,194],"high":[113],"base":[116],"Moreover,":[118],"guarantees":[121],"assume":[126],"weak":[131],"one":[134],"does":[136],"poor":[138],"job":[139],"ranking":[141],"documents":[142],"based":[143,214,225],"on":[144,215,226,254],"query.":[148],"In":[149,230],"practice,":[150],"course,":[152],"commercial":[153],"engines":[155],"highly":[157,193],"tuned":[158],"rankers,":[159],"desire":[161],"improve":[163],"upon":[164],"them":[165],"using":[166],"judgments":[169],"logs.":[172],"This":[173,199],"results":[174],"significant":[177],"correlation":[178],"between":[179],"position":[180],"relevance,":[182],"which":[183],"leads":[184],"overestimate":[189],"click":[190],"propensities":[191],"results,":[195],"reducing":[196],"ULTR's":[197],"effectiveness.":[198],"paper":[200],"first":[203],"demonstrate":[205],"problem":[207],"propensity":[209],"overestimation":[210],"algorithms,":[213],"causal":[217],"analysis.":[218],"We":[219,249],"develop":[220],"new":[222],"learning":[223],"objective":[224],"backdoor":[228],"adjustment.":[229],"addition,":[231],"we":[232],"introduce":[233],"Logging-Policy-aware":[235],"Propensity":[236],"(LPP)":[237],"model":[238],"learn":[242],"LPP":[243],"more":[246],"accurate":[247],"extensively":[250],"test":[251],"our":[252,262],"approach":[253],"two":[255],"public":[256],"benchmark":[257],"tasks":[258],"show":[260],"proposal":[263],"effective,":[265],"practical":[266],"significantly":[268],"outperforms":[269],"state":[271],"art.":[274]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}