{"id":"https://openalex.org/W7167901863","doi":"https://doi.org/10.1145/3805712.3809847","title":"CoCo: Conformal Confidence Suppression to Optimize Search Results","display_name":"CoCo: Conformal Confidence Suppression to Optimize Search Results","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7167901863","doi":"https://doi.org/10.1145/3805712.3809847"},"language":null,"primary_location":{"id":"doi:10.1145/3805712.3809847","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809847","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805712.3809847","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101553942","display_name":"Qin Zhou","orcid":"https://orcid.org/0000-0002-1641-772X"},"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":"Zhou Qin","raw_affiliation_strings":["Amazon, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-1641-772X","affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003009126","display_name":"Shuang Wu","orcid":"https://orcid.org/0000-0002-7551-7712"},"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":"Shuang Wu","raw_affiliation_strings":["Amazon, New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0003-1141-4153","affiliations":[{"raw_affiliation_string":"Amazon, New York, NY, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140385059","display_name":"Yoon Kim","orcid":"https://orcid.org/0009-0002-7599-564X"},"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":"Yoon Kim","raw_affiliation_strings":["Amazon, Palo Alto, CA, USA"],"raw_orcid":"https://orcid.org/0009-0002-7599-564X","affiliations":[{"raw_affiliation_string":"Amazon, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055349393","display_name":"Yi Liu","orcid":"https://orcid.org/0000-0001-7494-5339"},"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":"Yi Liu","raw_affiliation_strings":["Amazon, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7494-5339","affiliations":[{"raw_affiliation_string":"Amazon, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101735167","display_name":"Wenyang Liu","orcid":"https://orcid.org/0000-0002-5073-8623"},"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":"Wenyang Liu","raw_affiliation_strings":["Amazon, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5073-8623","affiliations":[{"raw_affiliation_string":"Amazon, Seattle, WA, USA","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94221792,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4058","last_page":"4063"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.3752000033855438,"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":0.3752000033855438,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.1485999971628189,"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/T10609","display_name":"Digital Marketing and Social Media","score":0.041600000113248825,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.45730000734329224},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.42980000376701355},{"id":"https://openalex.org/keywords/revenue","display_name":"Revenue","score":0.38199999928474426},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.3644999861717224},{"id":"https://openalex.org/keywords/content","display_name":"Content (measure theory)","score":0.359499990940094},{"id":"https://openalex.org/keywords/customer-lifetime-value","display_name":"Customer lifetime value","score":0.3467999994754791}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341000199317932},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4097999930381775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40619999170303345},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C130721881","wikidata":"https://www.wikidata.org/wiki/Q1146253","display_name":"Customer lifetime value","level":5,"score":0.3467999994754791},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C181622380","wikidata":"https://www.wikidata.org/wiki/Q26911","display_name":"Profit (economics)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805712.3809847","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809847","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805712.3809847","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809847","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2624816748","https://openalex.org/W2629213068","https://openalex.org/W3204398858","https://openalex.org/W4396944882"],"related_works":[],"abstract_inverted_index":{"Modern":[0],"e-commerce":[1],"recommendation":[2],"systems":[3],"aim":[4],"to":[5,51,63,81,136,153],"improve":[6],"customer":[7,98],"experience":[8],"by":[9],"ranking":[10],"content":[11,19,31,48,54],"on":[12,122],"search":[13],"results":[14],"page":[15],"(SRP).":[16],"However,":[17],"displaying":[18],"is":[20],"not":[21],"always":[22],"beneficial":[23],"for":[24,112],"customers":[25],"across":[26],"all":[27],"contexts;":[28],"even":[29],"top-ranked":[30],"can":[32],"be":[33],"irrelevant,":[34],"misleading,":[35],"or":[36],"redundant":[37],"in":[38,90,94,115,148],"certain":[39],"scenarios.":[40],"In":[41],"this":[42],"work,":[43],"we":[44],"propose":[45],"a":[46,103],"robust":[47],"suppression":[49],"mechanism":[50],"selectively":[52],"suppress":[53],"when":[55],"necessary.":[56],"Our":[57,100],"approach":[58],"leverages":[59],"causal":[60],"effect":[61,84],"learning":[62],"measure":[64],"the":[65,87],"incremental":[66],"value":[67],"of":[68],"showing":[69],"versus":[70],"suppressing":[71],"content.":[72],"Prior":[73],"work":[74,101],"uses":[75],"Conditional":[76],"Average":[77],"Treatment":[78],"Effect":[79],"(CATE)":[80],"estimate":[82],"treatment":[83],"without":[85],"considering":[86],"inherent":[88],"uncertainty":[89,114],"uplift":[91],"predictions,":[92],"resulting":[93],"over-suppression":[95],"and":[96,129,150],"degraded":[97],"experience.":[99],"introduces":[102],"novel":[104],"Conformal":[105],"Confidence":[106],"(CoCo)":[107],"suppressor":[108],"that":[109],"explicitly":[110],"accounts":[111],"prediction":[113],"uplift-based":[116],"modeling.":[117],"We":[118],"first":[119],"evaluate":[120],"it":[121],"synthetic":[123],"datasets":[124],"with":[125],"controlled":[126],"noise,":[127],"bias,":[128],"contexts.":[130],"Results":[131],"demonstrate":[132],"superior":[133],"performance":[134],"compared":[135,152],"baseline":[137],"approaches.":[138],"Subsequently,":[139],"our":[140],"online":[141],"traffic":[142],"tests":[143],"show":[144],"statistically":[145],"significant":[146],"improvements":[147],"revenue":[149],"profit":[151],"existing":[154],"methods.":[155]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-11T00:00:00"}
