{"id":"https://openalex.org/W2105089228","doi":null,"title":"Efficient Ranking from Pairwise Comparisons","display_name":"Efficient Ranking from Pairwise Comparisons","publication_year":2013,"publication_date":"2013-06-16","ids":{"openalex":"https://openalex.org/W2105089228","mag":"2105089228"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.363.8318","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.8318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/~flw/wauthier-jordan-jojic-icml13-web.pdf","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"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/A5076281878","display_name":"Fabian L. Wauthier","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fabian L. Wauthier","raw_affiliation_strings":["Computer Science Division, University of California, Berkeley, CA 94720, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Division, University of California, Berkeley, CA 94720, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049812527","display_name":"Michael I. Jordan","orcid":"https://orcid.org/0000-0001-8935-817X","observed_orcids":["https://orcid.org/0000-0001-8935-817X","https://orcid.org/0000-0002-2177-0405","https://orcid.org/0000-0002-5776-2626"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael I. Jordan","raw_affiliation_strings":["Computer Science Division, University of California, Berkeley, CA 94720, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Division, University of California, Berkeley, CA 94720, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108373424","display_name":"Neboj\u0161a Joji\u0107","orcid":"https://orcid.org/0009-0009-7115-2267","observed_orcids":["https://orcid.org/0009-0009-7115-2267"]},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neboj\u0161a Joji\u0107","raw_affiliation_strings":["[Microsoft Research, Redmond, WA 98052 USA.]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[Microsoft Research, Redmond, WA 98052 USA.]","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":38.3652,"has_fulltext":false,"cited_by_count":136,"citation_normalized_percentile":{"value":0.99703235,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"109","last_page":"117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10991","display_name":"Game Theory and Voting Systems","score":0.27950000762939453,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10991","display_name":"Game Theory and Voting Systems","score":0.27950000762939453,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.2791000008583069,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.13429999351501465,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-complexity-lower-bounds","display_name":"sample complexity lower bounds","score":0.7630000114440918},{"id":"https://openalex.org/keywords/kendall-tau-distance","display_name":"Kendall tau distance","score":0.7620000243186951},{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.5979999899864197},{"id":"https://openalex.org/keywords/ad-placement","display_name":"advertising placement","score":0.5559999942779541},{"id":"https://openalex.org/keywords/player-ranking","display_name":"player ranking","score":0.753000020980835},{"id":"https://openalex.org/keywords/binary-comparisons","display_name":"binary comparisons","score":0.375}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9000504016876221},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8794959783554077},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.6809886693954468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5897202491760254},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5810758471488953},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.496664822101593},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4831692576408386},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4642314314842224},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4379413425922394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4337926506996155},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.42790931463241577},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38437312841415405},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3674682378768921},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3443543016910553},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.363.8318","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.8318","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/~flw/wauthier-jordan-jojic-icml13-web.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.367.1288","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.1288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/~jordan/papers/wauthier-jordan-jojic-icml13.pdf","raw_type":"text"},{"id":"mag:2105089228","doi":null,"is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/proceedings/papers/v28/wauthier13.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Machine Learning","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W174211836","https://openalex.org/W182640454","https://openalex.org/W1584796555","https://openalex.org/W1600537614","https://openalex.org/W2033130993","https://openalex.org/W2035720976","https://openalex.org/W2038435918","https://openalex.org/W2060854861","https://openalex.org/W2060990051","https://openalex.org/W2066389806","https://openalex.org/W2069870183","https://openalex.org/W2098649057","https://openalex.org/W2099204387","https://openalex.org/W2107890099","https://openalex.org/W2108807072","https://openalex.org/W2111296615","https://openalex.org/W2117990954","https://openalex.org/W2121824931","https://openalex.org/W2122350398","https://openalex.org/W2149166361","https://openalex.org/W2149509651","https://openalex.org/W2395021187","https://openalex.org/W2404067076","https://openalex.org/W2552323286","https://openalex.org/W2913924976","https://openalex.org/W2963020293","https://openalex.org/W2988119488"],"related_works":["https://openalex.org/W2781702464","https://openalex.org/W2155817812","https://openalex.org/W2970664163","https://openalex.org/W2152408869","https://openalex.org/W2998909337","https://openalex.org/W7095389395","https://openalex.org/W2887146843","https://openalex.org/W3008646423","https://openalex.org/W4409983015","https://openalex.org/W3022970899"],"abstract_inverted_index":{"The":[0],"ranking":[1,62,131,141],"of":[2,42,118],"n":[3],"objects":[4],"based":[5],"on":[6,75],"pairwise":[7,33],"comparisons":[8,34,45,122],"is":[9,46],"a":[10,40,61,133],"core":[11],"machine":[12],"learning":[13],"problem,":[14],"arising":[15],"in":[16,112,132],"recommender":[17],"systems,":[18],"ad":[19],"placement,":[20],"player":[21],"ranking,":[22,157],"biological":[23],"applications":[24],"and":[25,48,155],"others.":[26],"In":[27,96],"many":[28],"practical":[29],"situations":[30],"the":[31,54,76,107,129,137,140,145,148],"true":[32,130],"cannot":[35],"be":[36,57],"actively":[37],"measured,":[38,124],"but":[39],"subset":[41],"all":[43],"n(n\u22121)/2":[44],"passively":[47],"noisily":[49],"observed.":[50],"Optimization":[51],"algorithms":[52,104],"(e.g.,":[53],"SVM)":[55],"could":[56],"used":[58],"to":[59,82,89,91,153],"predict":[60],"with":[63,158],"fixed":[64],"expected":[65],"Kendall":[66],"tau":[67],"distance,":[68],"while":[69,136],"achieving":[70],"an":[71,116],"\u2126(n)":[72,109],"lower":[73,110],"bound":[74,111],"corresponding":[77],"sample":[78],"complexity.":[79],"However,":[80],"due":[81],"their":[83],"centralized":[84],"structure":[85],"they":[86],"are":[87,123],"difficult":[88],"extend":[90],"online":[92,154],"or":[93],"distributed":[94,156],"settings.":[95],"this":[97],"paper":[98],"we":[99],"show":[100],"that":[101],"much":[102],"simpler":[103],"can":[105],"match":[106],"same":[108],"expectation.":[113],"Furthermore,":[114],"if":[115],"average":[117],"O(n":[119],"log(n))":[120],"binary":[121],"then":[125],"one":[126],"algorithm":[127],"recovers":[128],"uniform":[134],"sense,":[135],"other":[138],"predicts":[139],"more":[142],"accurately":[143],"near":[144],"top":[146],"than":[147],"bottom.":[149],"We":[150],"discuss":[151],"extensions":[152],"benefits":[159],"over":[160],"traditional":[161],"alternatives.":[162],"1.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":23},{"year":2015,"cited_by_count":17},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}