{"id":"https://openalex.org/W4414449967","doi":"https://doi.org/10.1145/3746252.3761377","title":"Addressing Personalized Bias for Unbiased Learning to Rank","display_name":"Addressing Personalized Bias for Unbiased Learning to Rank","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4414449967","doi":"https://doi.org/10.1145/3746252.3761377"},"language":"en","primary_location":{"id":"doi:10.1145/3746252.3761377","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.20798","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102645619","display_name":"Zechun Niu","orcid":"https://orcid.org/0009-0009-7954-3713"},"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":"Zechun Niu","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-7954-3713","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102857942","display_name":"Lang Mei","orcid":"https://orcid.org/0000-0002-7960-3036"},"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":"Lang Mei","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7960-3036","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112136876","display_name":"Liu Yang","orcid":"https://orcid.org/0000-0002-0154-9957"},"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":"Liu Yang","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0154-9957","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102736403","display_name":"Ziyuan Zhao","orcid":"https://orcid.org/0009-0000-4742-4116"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyuan Zhao","raw_affiliation_strings":["Search Algorithm Group, WeChat, Tencent, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0000-4742-4116","affiliations":[{"raw_affiliation_string":"Search Algorithm Group, WeChat, Tencent, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101766249","display_name":"Qiang Yan","orcid":"https://orcid.org/0000-0002-7328-2278"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Yan","raw_affiliation_strings":["Search Algorithm Group, WeChat, Tencent, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-7328-2278","affiliations":[{"raw_affiliation_string":"Search Algorithm Group, WeChat, Tencent, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072119199","display_name":"Jiaxin Mao","orcid":"https://orcid.org/0000-0002-9257-5498"},"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":"Jiaxin Mao","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China, China"],"raw_orcid":"https://orcid.org/0000-0002-9257-5498","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025631695","display_name":"Ji-Rong Wen","orcid":"https://orcid.org/0000-0002-9777-9676"},"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":"Ji-Rong Wen","raw_affiliation_strings":["Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9777-9676","affiliations":[{"raw_affiliation_string":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88679003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2222","last_page":"2231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.8072999715805054,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.8072999715805054,"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/estimator","display_name":"Estimator","score":0.656499981880188},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6261000037193298},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6111000180244446},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5849000215530396},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5788000226020813},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.520799994468689},{"id":"https://openalex.org/keywords/mean-reciprocal-rank","display_name":"Mean reciprocal rank","score":0.4961000084877014},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.4657999873161316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7117000222206116},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.656499981880188},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6261000037193298},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6111000180244446},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5849000215530396},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5788000226020813},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C44083865","wikidata":"https://www.wikidata.org/wiki/Q3853443","display_name":"Mean reciprocal rank","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.4657999873161316},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39590001106262207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36410000920295715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35089999437332153},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C2777601897","wikidata":"https://www.wikidata.org/wiki/Q3409113","display_name":"Presentation (obstetrics)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C2776945383","wikidata":"https://www.wikidata.org/wiki/Q7170667","display_name":"Personalized search","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C2909318450","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Unbiased Estimation","level":3,"score":0.2612000107765198}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3746252.3761377","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.20798","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.20798","pdf_url":"https://arxiv.org/pdf/2508.20798","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.20798","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.20798","pdf_url":"https://arxiv.org/pdf/2508.20798","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unbiased":[0],"learning":[1],"to":[2,7,155,176],"rank":[3],"(ULTR),":[4],"which":[5,84],"aims":[6],"learn":[8],"unbiased":[9,166],"ranking":[10],"models":[11,140],"from":[12,57],"biased":[13,106],"user":[14,144],"behavior":[15,53],"logs,":[16],"plays":[17],"an":[18,58],"important":[19],"role":[20],"in":[21,34,67],"Web":[22],"search.":[23],"Previous":[24],"research":[25],"on":[26,202],"ULTR":[27,82,89],"has":[28],"studied":[29],"a":[30,92,124,129,182,207],"variety":[31],"of":[32,96,118,143,180,194],"biases":[33],"users'":[35],"clicks,":[36],"such":[37,123],"as":[38],"position":[39],"bias,":[40,42,126],"presentation":[41],"and":[43,70,115,150,171,206],"outlier":[44],"bias.":[45],"However,":[46],"existing":[47,102],"work":[48],"often":[49],"assumes":[50],"that":[51,101,161],"the":[52,62,81,87,141,162,177,192],"logs":[54],"are":[55,105],"collected":[56],"''average''":[59],"user,":[60],"neglecting":[61],"differences":[63],"between":[64],"different":[65,108,111],"users":[66,109],"their":[68],"search":[69],"browsing":[71,145],"behaviors.":[72],"In":[73],"this":[74,97],"paper,":[75],"we":[76,85,99,127,189],"introduce":[77],"personalized":[78,116,125],"factors":[79],"into":[80],"framework,":[83],"term":[86],"user-aware":[88,131,163,196],"problem.":[90],"Through":[91],"formal":[93],"causal":[94],"analysis":[95],"problem,":[98],"demonstrate":[100],"user-oblivious":[103],"methods":[104],"when":[107],"have":[110],"preferences":[112],"over":[113],"queries":[114],"propensities":[117],"examining":[119],"documents.":[120],"To":[121],"address":[122],"propose":[128],"novel":[130],"inverse-propensity-score":[132],"estimator":[133,164,197],"for":[134,147,185],"learning-to-rank":[135],"objectives.":[136],"Specifically,":[137],"our":[138,195],"approach":[139],"distribution":[142],"behaviors":[146],"each":[148,186],"query":[149],"aggregates":[151],"user-weighted":[152],"examination":[153],"probabilities":[154],"determine":[156],"propensities.":[157],"We":[158],"theoretically":[159],"prove":[160],"is":[165],"under":[167],"some":[168],"mild":[169],"assumptions":[170],"shows":[172],"lower":[173],"variance":[174],"compared":[175],"straightforward":[178],"way":[179],"calculating":[181],"user-dependent":[183],"propensity":[184],"impression.":[187],"Finally,":[188],"empirically":[190],"verify":[191],"effectiveness":[193],"by":[198],"conducting":[199],"extensive":[200],"experiments":[201],"two":[203],"semi-synthetic":[204],"datasets":[205],"real-world":[208],"dataset.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}