{"id":"https://openalex.org/W3115584932","doi":"https://doi.org/10.1145/3437963.3441772","title":"Bias-Variance Decomposition for Ranking","display_name":"Bias-Variance Decomposition for Ranking","publication_year":2021,"publication_date":"2021-03-06","ids":{"openalex":"https://openalex.org/W3115584932","doi":"https://doi.org/10.1145/3437963.3441772","mag":"3115584932"},"language":"en","primary_location":{"id":"doi:10.1145/3437963.3441772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3437963.3441772","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3437963.3441772","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 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3437963.3441772","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015752938","display_name":"Pannaga Shivaswamy","orcid":null},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pannaga Shivaswamy","raw_affiliation_strings":["Netflix Inc., Los Gatos, CA, USA"],"affiliations":[{"raw_affiliation_string":"Netflix Inc., Los Gatos, CA, USA","institution_ids":["https://openalex.org/I869089601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062342289","display_name":"Ashok Chandrashekar","orcid":"https://orcid.org/0009-0007-1914-8354"},"institutions":[{"id":"https://openalex.org/I869089601","display_name":"Netflix (United States)","ror":"https://ror.org/0197qw696","country_code":"US","type":"company","lineage":["https://openalex.org/I869089601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashok Chandrashekar","raw_affiliation_strings":["Netflix Inc., Los Gatos, CA, USA"],"affiliations":[{"raw_affiliation_string":"Netflix Inc., Los Gatos, CA, USA","institution_ids":["https://openalex.org/I869089601"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5015752938"],"corresponding_institution_ids":["https://openalex.org/I869089601"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70871467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"472","last_page":"480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9973999857902527,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9973999857902527,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9970999956130981,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9944000244140625,"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/ranking","display_name":"Ranking (information retrieval)","score":0.822387158870697},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.7525432109832764},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6756628155708313},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6622689366340637},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6076711416244507},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6029436588287354},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5234876871109009},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.4817090630531311},{"id":"https://openalex.org/keywords/variance-decomposition-of-forecast-errors","display_name":"Variance decomposition of forecast errors","score":0.4783563017845154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4762982428073883},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4544936716556549},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41484513878822327},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4112803637981415},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3955899178981781},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31699395179748535}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.822387158870697},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.7525432109832764},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6756628155708313},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6622689366340637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6076711416244507},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6029436588287354},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5234876871109009},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.4817090630531311},{"id":"https://openalex.org/C89715816","wikidata":"https://www.wikidata.org/wiki/Q7915763","display_name":"Variance decomposition of forecast errors","level":2,"score":0.4783563017845154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4762982428073883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4544936716556549},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41484513878822327},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4112803637981415},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3955899178981781},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31699395179748535},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3437963.3441772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3437963.3441772","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3437963.3441772","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 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3437963.3441772","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3437963.3441772","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3437963.3441772","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 14th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3115584932.pdf","grobid_xml":"https://content.openalex.org/works/W3115584932.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1491993603","https://openalex.org/W1516193414","https://openalex.org/W1521062204","https://openalex.org/W1530210183","https://openalex.org/W1532325895","https://openalex.org/W1973435495","https://openalex.org/W1979697714","https://openalex.org/W1981379119","https://openalex.org/W2047221353","https://openalex.org/W2058475745","https://openalex.org/W2076118331","https://openalex.org/W2108862644","https://openalex.org/W2115584760","https://openalex.org/W2143331230","https://openalex.org/W2210543184","https://openalex.org/W2340526403","https://openalex.org/W2507134384","https://openalex.org/W2512971201","https://openalex.org/W2769473018","https://openalex.org/W2804927761","https://openalex.org/W2955229766","https://openalex.org/W2963518130","https://openalex.org/W3008906732","https://openalex.org/W3028722847","https://openalex.org/W3105035347","https://openalex.org/W4213009331","https://openalex.org/W4234477270","https://openalex.org/W4298304654"],"related_works":["https://openalex.org/W2011472225","https://openalex.org/W2767338541","https://openalex.org/W3000057026","https://openalex.org/W3048565508","https://openalex.org/W3163984363","https://openalex.org/W3127142483","https://openalex.org/W4385565564","https://openalex.org/W2898073868","https://openalex.org/W4390446658","https://openalex.org/W2138488530"],"abstract_inverted_index":{"In":[0,16],"machine":[1],"learning":[2,10],"and":[3,6,46,62,71,83],"statistics,":[4],"bias":[5,45,70],"variance":[7,47,72,151,160],"of":[8,31,33,44,52,112,126],"supervised":[9],"models":[11],"are":[12],"well":[13],"studied":[14],"concepts.":[15],"this":[17],"work,":[18],"we":[19,143],"try":[20],"to":[21,75,103],"better":[22],"understand":[23],"how":[24],"these":[25],"concepts":[26],"apply":[27],"in":[28,115,152,158],"the":[29,76,80,95,98,110,113,124,127,132,153,165],"context":[30],"ranking":[32,57,64,93,128,166],"items":[34],"(e.g.,":[35],"for":[36,79,135],"web-search":[37],"or":[38],"recommender":[39],"systems).":[40],"We":[41,54,108],"define":[42],"notions":[43],"directly":[48,121],"on":[49,164],"pairwise":[50],"ordering":[51],"items.":[53],"show":[55,144],"that":[56,86],"disagreements":[58],"between":[59,118],"true":[60],"orderings":[61],"a":[63,105],"function":[65],"can":[66,138],"be":[67],"decomposed":[68],"into":[69],"components":[73],"akin":[74],"similar":[77,106],"decomposition":[78,125],"squared":[81],"loss":[82,129,133],"other":[84],"losses":[85],"have":[87],"been":[88],"previously":[89],"studied.":[90],"The":[91],"popular":[92],"metric,":[94],"area":[96],"under":[97],"curve":[99],"(AUC),":[100],"is":[101],"shown":[102],"admit":[104],"decomposition.":[107],"demonstrate":[109],"utility":[111],"framework":[114],"understanding":[116],"differences":[117],"models.":[119],"Further,":[120],"looking":[122],"at":[123],"rather":[130],"than":[131],"used":[134],"model":[136,147],"fitting":[137],"reveal":[139],"surprising":[140],"insights.":[141],"Specifically,":[142],"examples":[145],"where":[146],"training":[148],"achieves":[149],"low":[150],"traditional":[154],"sense,":[155],"yet":[156],"results":[157],"high":[159,162],"(and":[161],"error)":[163],"task.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}