{"id":"https://openalex.org/W2896921640","doi":"https://doi.org/10.1145/3269206.3269260","title":"Calibration","display_name":"Calibration","publication_year":2018,"publication_date":"2018-10-17","ids":{"openalex":"https://openalex.org/W2896921640","doi":"https://doi.org/10.1145/3269206.3269260","mag":"2896921640"},"language":"en","primary_location":{"id":"doi:10.1145/3269206.3269260","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3269260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","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/A5026703230","display_name":"Alexey Borisov","orcid":"https://orcid.org/0000-0002-3837-4150"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alexey Borisov","raw_affiliation_strings":["Yandex & University of Amsterdam, Moscow, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Yandex & University of Amsterdam, Moscow, Russian Fed","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026947055","display_name":"Julia Kiseleva","orcid":"https://orcid.org/0000-0002-3789-9588"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Julia Kiseleva","raw_affiliation_strings":["UserSat.com & University of Amsterdam, Amsterdam, Netherlands"],"affiliations":[{"raw_affiliation_string":"UserSat.com & University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103028673","display_name":"Ilya Markov","orcid":"https://orcid.org/0000-0001-9221-3043"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ilya Markov","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031439294","display_name":"Maarten de Rijke","orcid":"https://orcid.org/0000-0002-1086-0202"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Maarten de Rijke","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026703230"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1816,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84646265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1503","last_page":"1506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9991999864578247,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958999752998352,"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/hyperparameter","display_name":"Hyperparameter","score":0.8173879384994507},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.8032666444778442},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.7441624402999878},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7337181568145752},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5698572993278503},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5656555891036987},{"id":"https://openalex.org/keywords/isotonic-regression","display_name":"Isotonic regression","score":0.562153697013855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5305581092834473},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5052998661994934},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4635687470436096},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29495278000831604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2016066610813141},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.15350914001464844}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8173879384994507},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.8032666444778442},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.7441624402999878},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7337181568145752},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5698572993278503},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5656555891036987},{"id":"https://openalex.org/C17418463","wikidata":"https://www.wikidata.org/wiki/Q3455874","display_name":"Isotonic regression","level":3,"score":0.562153697013855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5305581092834473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5052998661994934},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4635687470436096},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29495278000831604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2016066610813141},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.15350914001464844},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3269206.3269260","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3269260","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/e8159067-c9d1-4649-9ac3-1e7c58e93973","is_oa":false,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/calibration-a-simple-way-to-improve-click-models(e8159067-c9d1-4649-9ac3-1e7c58e93973).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Borisov, A, Kiseleva, J, Markov, I & de Rijke, M 2018, Calibration: A Simple Way to Improve Click Models. in CIKM'18 : proceedings of the 2018 ACM International Conference on Information and Knowledge Management : October 22-26, 2018, Torino, Italy. New York, NY, pp. 1503-1506, 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 22/10/18. https://doi.org/10.1145/3269206.3269260","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:uvapub:oai:dare.uva.nl:publications/e8159067-c9d1-4649-9ac3-1e7c58e93973","is_oa":false,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/calibration-a-simple-way-to-improve-click-models(e8159067-c9d1-4649-9ac3-1e7c58e93973).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CIKM'18: proceedings of the 2018 ACM International Conference on Information and Knowledge Management : October 22-26, 2018, Torino, Italy, 1503 - 1506","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8543999253","display_name":null,"funder_award_id":"CI-14-25","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1213593253","https://openalex.org/W1507029541","https://openalex.org/W1517796226","https://openalex.org/W1549656520","https://openalex.org/W1598033630","https://openalex.org/W1618905105","https://openalex.org/W1804792716","https://openalex.org/W1992549066","https://openalex.org/W2012942264","https://openalex.org/W2016511206","https://openalex.org/W2026784708","https://openalex.org/W2047221353","https://openalex.org/W2098824882","https://openalex.org/W2099213975","https://openalex.org/W2102693936","https://openalex.org/W2144677027","https://openalex.org/W2152314154","https://openalex.org/W2154739689","https://openalex.org/W2402441596","https://openalex.org/W2795929191","https://openalex.org/W4285719527","https://openalex.org/W4302322961"],"related_works":["https://openalex.org/W2376415519","https://openalex.org/W1601381279","https://openalex.org/W4294769427","https://openalex.org/W4225667838","https://openalex.org/W1895908943","https://openalex.org/W4281893144","https://openalex.org/W2374747083","https://openalex.org/W2030978506","https://openalex.org/W54011351","https://openalex.org/W2064237320"],"abstract_inverted_index":{"We":[0],"show":[1,52],"that":[2,18,53,69,108],"click":[3,21,58,72,87,116],"models":[4,59,73,88],"trained":[5,60],"with":[6,26,61],"suboptimal":[7,62],"hyperparameters":[8,63],"suffer":[9],"from":[10],"the":[11,27,33,77,82,115],"issue":[12],"of":[13,30,66,79,85,91,114],"bad":[14],"calibration.":[15],"This":[16],"means":[17],"their":[19,92,101],"predicted":[20],"probabilities":[22],"do":[23],"not":[24,100],"agree":[25],"observed":[28],"proportions":[29],"clicks":[31],"in":[32,64,89],"held-out":[34],"data.":[35],"To":[36],"repair":[37],"this":[38],"discrepancy,":[39],"we":[40,106],"adapt":[41],"a":[42,111],"non-parametric":[43],"calibration":[44,109],"method":[45],"called":[46],"isotonic":[47,54],"regression.":[48],"Our":[49],"experimental":[50],"results":[51],"regression":[55],"significantly":[56],"improves":[57],"terms":[65,90],"perplexity,":[67],"and":[68],"it":[70],"makes":[71],"less":[74],"sensitive":[75],"to":[76],"choice":[78],"hyperparameters.":[80],"Interestingly,":[81],"relative":[83],"ranking":[84],"existing":[86],"predictive":[93],"performance":[94],"changes":[95],"depending":[96],"on":[97],"whether":[98],"or":[99],"predictions":[102],"are":[103],"calibrated.":[104],"Therefore,":[105],"advocate":[107],"becomes":[110],"mandatory":[112],"part":[113],"model":[117],"evaluation":[118],"protocol.":[119]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-15T09:29:46.208133","created_date":"2018-10-26T00:00:00"}