{"id":"https://openalex.org/W2279385734","doi":"https://doi.org/10.1145/2835776.2835804","title":"Multileave Gradient Descent for Fast Online Learning to Rank","display_name":"Multileave Gradient Descent for Fast Online Learning to Rank","publication_year":2016,"publication_date":"2016-02-04","ids":{"openalex":"https://openalex.org/W2279385734","doi":"https://doi.org/10.1145/2835776.2835804","mag":"2279385734"},"language":"en","primary_location":{"id":"doi:10.1145/2835776.2835804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2835776.2835804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth 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":"green","oa_url":"https://dare.uva.nl/personal/pure/en/publications/multileave-gradient-descent-for-fast-online-learning-to-rank(d384f922-e64f-4be4-9667-1d9504f7c882).html","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086449136","display_name":"Anne Schuth","orcid":"https://orcid.org/0000-0002-5841-2134"},"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":true,"raw_author_name":"Anne Schuth","raw_affiliation_strings":["University of Amsterdam, Amsterdam, The Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002072527","display_name":"Harrie Oosterhuis","orcid":"https://orcid.org/0000-0002-0458-9233"},"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":"Harrie Oosterhuis","raw_affiliation_strings":["University of Amsterdam, Amsterdam, The Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056879203","display_name":"Shimon Whiteson","orcid":null},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shimon Whiteson","raw_affiliation_strings":["University of Oxford, Oxford, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"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"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Maarten de Rijke","raw_affiliation_strings":["University of Amsterdam, Amsterdam, The Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, The Netherlands","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086449136"],"corresponding_institution_ids":["https://openalex.org/I887064364"],"apc_list":null,"apc_paid":null,"fwci":13.4363,"has_fulltext":false,"cited_by_count":87,"citation_normalized_percentile":{"value":0.9875913,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"457","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9977999925613403,"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"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9977999925613403,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.9929999709129333,"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/computer-science","display_name":"Computer science","score":0.7316712737083435},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.6865114569664001},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6825160384178162},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6509720683097839},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6010322570800781},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5682497024536133},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5545427799224854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.520541787147522},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.518548309803009},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.48698100447654724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4407607316970825},{"id":"https://openalex.org/keywords/descent","display_name":"Descent (aeronautics)","score":0.4264100193977356},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38046175241470337},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16099151968955994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7316712737083435},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.6865114569664001},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6825160384178162},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6509720683097839},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6010322570800781},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5682497024536133},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5545427799224854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.520541787147522},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.518548309803009},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.48698100447654724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4407607316970825},{"id":"https://openalex.org/C2776637919","wikidata":"https://www.wikidata.org/wiki/Q624380","display_name":"Descent (aeronautics)","level":2,"score":0.4264100193977356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38046175241470337},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16099151968955994},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/2835776.2835804","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2835776.2835804","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire/d384f922-e64f-4be4-9667-1d9504f7c882","is_oa":true,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/multileave-gradient-descent-for-fast-online-learning-to-rank(d384f922-e64f-4be4-9667-1d9504f7c882).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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Schuth, A, Oosterhuis, H, Whiteson, S & de Rijke, M 2016, Multileave Gradient Descent for Fast Online Learning to Rank. in WSDM'16 : proceedings of the Ninth ACM International Conference on Web Search and Data Mining : February 22-25, 2016, San Francisco, CA, USA. Association for Computing Machinery, New York, NY, pp. 457-466, WSDM 2016: The 9th International Conference on Web Search and Data Mining, 22/02/16. https://doi.org/10.1145/2835776.2835804","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.707.5123","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.707.5123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.ox.ac.uk/people/shimon.whiteson/pubs/schuthwsdm16.pdf","raw_type":"text"},{"id":"pmh:oai:dare.uva.nl:Publications/d384f922-e64f-4be4-9667-1d9504f7c882","is_oa":false,"landing_page_url":null,"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":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:dare.uva.nl:openaire/d384f922-e64f-4be4-9667-1d9504f7c882","is_oa":true,"landing_page_url":"https://dare.uva.nl/personal/pure/en/publications/multileave-gradient-descent-for-fast-online-learning-to-rank(d384f922-e64f-4be4-9667-1d9504f7c882).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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Schuth, A, Oosterhuis, H, Whiteson, S & de Rijke, M 2016, Multileave Gradient Descent for Fast Online Learning to Rank. in WSDM'16 : proceedings of the Ninth ACM International Conference on Web Search and Data Mining : February 22-25, 2016, San Francisco, CA, USA. Association for Computing Machinery, New York, NY, pp. 457-466, WSDM 2016: The 9th International Conference on Web Search and Data Mining, 22/02/16. https://doi.org/10.1145/2835776.2835804","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W135190683","https://openalex.org/W1495821370","https://openalex.org/W1515851193","https://openalex.org/W1973435495","https://openalex.org/W1974360117","https://openalex.org/W1980224846","https://openalex.org/W1982530130","https://openalex.org/W1984770088","https://openalex.org/W1988344511","https://openalex.org/W2003473029","https://openalex.org/W2009979684","https://openalex.org/W2022995284","https://openalex.org/W2035569891","https://openalex.org/W2037140704","https://openalex.org/W2041576073","https://openalex.org/W2044493620","https://openalex.org/W2047221353","https://openalex.org/W2069870183","https://openalex.org/W2094790959","https://openalex.org/W2110228583","https://openalex.org/W2115711489","https://openalex.org/W2121863487","https://openalex.org/W2126690248","https://openalex.org/W2129245267","https://openalex.org/W2130145422","https://openalex.org/W2138909795","https://openalex.org/W2147892741","https://openalex.org/W2148224612","https://openalex.org/W2149427297","https://openalex.org/W2154739689","https://openalex.org/W2156541733","https://openalex.org/W2159665776","https://openalex.org/W2163996478","https://openalex.org/W2165387257","https://openalex.org/W2407295445","https://openalex.org/W4300415226"],"related_works":["https://openalex.org/W3127142483","https://openalex.org/W4385565564","https://openalex.org/W2898073868","https://openalex.org/W4390446658","https://openalex.org/W2138488530","https://openalex.org/W2971071571","https://openalex.org/W2798835721","https://openalex.org/W2922169395","https://openalex.org/W2387658907","https://openalex.org/W2895097035"],"abstract_inverted_index":{"Modern":[0],"search":[1,37,190],"systems":[2],"are":[3,168],"based":[4,69],"on":[5,71],"dozens":[6],"or":[7],"even":[8],"hundreds":[9],"of":[10,27,116,119,132,147,153],"ranking":[11],"features.":[12],"The":[13],"dueling":[14],"bandit":[15],"gradient":[16,98],"descent":[17,99],"(DBGD)":[18],"algorithm":[19,95],"has":[20],"been":[21],"shown":[22],"to":[23,45,83,93,104,161,173,179,182,192,196],"effectively":[24],"learn":[25,105],"combinations":[26],"these":[28],"features":[29],"solely":[30],"from":[31,106],"user":[32,156,199],"interactions.":[33],"DBGD":[34,103,135],"explores":[35],"the":[36,46,137],"space":[38],"by":[39],"comparing":[40],"a":[41,64,84,114,121],"possibly":[42,183],"improved":[43],"ranker":[44],"current":[47],"production":[48],"ranker.":[49,87],"To":[50],"this":[51],"end,":[52],"it":[53,79],"uses":[54],"interleaved":[55],"comparison":[56,109],"methods,":[57],"which":[58],"can":[59,80,112],"infer":[60],"with":[61],"high":[62],"sensitivity":[63],"preference":[65],"between":[66],"two":[67],"rankings":[68,117],"only":[70,82],"interaction":[72,157],"data.":[73],"A":[74],"limiting":[75],"factor":[76],"is":[77,150,159],"that":[78,101,111,126,151],"compare":[81,113],"single":[85],"exploratory":[86],"We":[88,123],"propose":[89],"an":[90],"online":[91,171],"learning":[92,172],"rank":[94],"called":[96],"multileave":[97],"(MGD)":[100],"extends":[102],"so-called":[107],"multileaved":[108,166],"methods":[110],"set":[115],"instead":[118],"merely":[120],"pair.":[122],"show":[124],"experimentally":[125],"MGD":[127],"allows":[128,189],"for":[129,139],"better":[130],"selection":[131],"candidates":[133],"than":[134],"without":[136],"need":[138,178],"more":[140,194],"comparisons":[141,167],"involving":[142],"users.":[143],"An":[144],"important":[145],"implication":[146],"our":[148,187],"results":[149],"orders":[152],"magnitude":[154],"less":[155],"data":[158],"required":[160],"find":[162],"good":[163],"rankers":[164,185],"when":[165],"used":[169],"within":[170],"rank.":[174],"Hence,":[175],"fewer":[176],"users":[177],"be":[180],"exposed":[181],"inferior":[184],"and":[186],"method":[188],"engines":[191],"adapt":[193],"quickly":[195],"changes":[197],"in":[198],"preferences.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}