{"id":"https://openalex.org/W2908412680","doi":"https://doi.org/10.1145/3289600.3291006","title":"WassRank","display_name":"WassRank","publication_year":2019,"publication_date":"2019-01-30","ids":{"openalex":"https://openalex.org/W2908412680","doi":"https://doi.org/10.1145/3289600.3291006","mag":"2908412680"},"language":"en","primary_location":{"id":"doi:10.1145/3289600.3291006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289600.3291006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth 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":"http://eprints.gla.ac.uk/182378/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101744904","display_name":"Hai-Tao Yu","orcid":"https://orcid.org/0000-0002-1569-8507"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hai-Tao Yu","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079733597","display_name":"Adam Jatowt","orcid":"https://orcid.org/0000-0001-7235-0665"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Adam Jatowt","raw_affiliation_strings":["Kyoto University, Kyoto, Japan"],"affiliations":[{"raw_affiliation_string":"Kyoto University, Kyoto, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017282307","display_name":"Hideo Joho","orcid":"https://orcid.org/0000-0002-6611-652X"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Joho","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069702331","display_name":"Joemon M. Jose","orcid":"https://orcid.org/0000-0001-9228-1759"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Joemon M. Jose","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442770","display_name":"Xiao Yang","orcid":"https://orcid.org/0000-0003-1675-1995"},"institutions":[{"id":"https://openalex.org/I1303153112","display_name":"European Bioinformatics Institute","ror":"https://ror.org/02catss52","country_code":"GB","type":"facility","lineage":["https://openalex.org/I1303153112","https://openalex.org/I4210138560"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiao Yang","raw_affiliation_strings":["European Bioinformatics Institute, Cambridge, United Kingdom"],"affiliations":[{"raw_affiliation_string":"European Bioinformatics Institute, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I1303153112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100336450","display_name":"Long Chen","orcid":"https://orcid.org/0000-0003-4925-0572"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["University of Glasgow, Glasgow, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Glasgow, Glasgow, United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101744904"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":1.3159,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.83962942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"24","last_page":"32"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9983999729156494,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9980999827384949,"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":0.8599551916122437},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.785968005657196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7170405387878418},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.712588369846344},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.686170220375061},{"id":"https://openalex.org/keywords/ranking-svm","display_name":"Ranking SVM","score":0.614097535610199},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5320917963981628},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4409262239933014},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4397311210632324},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4164445698261261},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3765745460987091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36360257863998413},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3309527039527893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2062385082244873},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07888072729110718}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8599551916122437},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.785968005657196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7170405387878418},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.712588369846344},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.686170220375061},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.614097535610199},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5320917963981628},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4409262239933014},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4397311210632324},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4164445698261261},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3765745460987091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36360257863998413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3309527039527893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2062385082244873},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07888072729110718},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3289600.3291006","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289600.3291006","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.gla.ac.uk:182378","is_oa":true,"landing_page_url":"http://eprints.gla.ac.uk/182378/","pdf_url":null,"source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":{"id":"pmh:oai:eprints.gla.ac.uk:182378","is_oa":true,"landing_page_url":"http://eprints.gla.ac.uk/182378/","pdf_url":null,"source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W30273491","https://openalex.org/W1482214997","https://openalex.org/W1500474910","https://openalex.org/W1708221419","https://openalex.org/W1973435495","https://openalex.org/W1974160425","https://openalex.org/W1990805993","https://openalex.org/W2001832483","https://openalex.org/W2009077327","https://openalex.org/W2019044299","https://openalex.org/W2023508744","https://openalex.org/W2035720976","https://openalex.org/W2047221353","https://openalex.org/W2047778511","https://openalex.org/W2059001985","https://openalex.org/W2067802667","https://openalex.org/W2069870183","https://openalex.org/W2091158010","https://openalex.org/W2103179193","https://openalex.org/W2108862644","https://openalex.org/W2111296615","https://openalex.org/W2113640060","https://openalex.org/W2115843962","https://openalex.org/W2127176025","https://openalex.org/W2127840217","https://openalex.org/W2128877075","https://openalex.org/W2136189984","https://openalex.org/W2140472169","https://openalex.org/W2142537246","https://openalex.org/W2142575165","https://openalex.org/W2143331230","https://openalex.org/W2144211451","https://openalex.org/W2149498267","https://openalex.org/W2151592910","https://openalex.org/W2158131535","https://openalex.org/W2159545104","https://openalex.org/W2162059449","https://openalex.org/W2166546789","https://openalex.org/W2166706236","https://openalex.org/W2186845332","https://openalex.org/W2312355711","https://openalex.org/W2338325072","https://openalex.org/W2404429280","https://openalex.org/W2417426974","https://openalex.org/W2536015822","https://openalex.org/W2539247542","https://openalex.org/W2552322480","https://openalex.org/W2556873163","https://openalex.org/W2579923771","https://openalex.org/W2739748921","https://openalex.org/W2774514250","https://openalex.org/W2793477525","https://openalex.org/W2884475480","https://openalex.org/W2919115771","https://openalex.org/W2951359136","https://openalex.org/W2951798229","https://openalex.org/W3011144947","https://openalex.org/W4285719527","https://openalex.org/W4312943126"],"related_works":["https://openalex.org/W3127142483","https://openalex.org/W2138488530","https://openalex.org/W2031468273","https://openalex.org/W2370100764","https://openalex.org/W4385565564","https://openalex.org/W2387658907","https://openalex.org/W2351112195","https://openalex.org/W4378464883","https://openalex.org/W2898073868","https://openalex.org/W2110822809"],"abstract_inverted_index":{"Learning":[0],"to":[1,55,68,103,168],"rank":[2,251],"has":[3,8],"been":[4],"intensively":[5],"studied":[6],"and":[7,21,89,185,235,247,263],"shown":[9],"great":[10],"value":[11],"in":[12,39,243,265],"many":[13],"fields,":[14],"such":[15],"as":[16,45,56,113,130],"web":[17],"search,":[18],"question":[19],"answering":[20],"recommender":[22],"systems.":[23],"This":[24],"paper":[25],"focuses":[26],"on":[27,215],"listwise":[28,63,125,228],"document":[29,64],"ranking,":[30],"where":[31],"all":[32],"documents":[33,180,192],"associated":[34],"with":[35,119,181,193,225],"the":[36,40,46,60,69,73,79,85,90,104,131,141,151,159,163,165,169,174,187,194,205,254],"same":[37,195],"query":[38,86],"training":[41],"data":[42],"are":[43,270],"used":[44],"input.":[47],"We":[48],"propose":[49],"a":[50,114,211],"novel":[51],"ranking":[52,65,75,126,229],"method,":[53],"referred":[54],"WassRank,":[57,208],"under":[58],"which":[59],"problem":[61],"of":[62,71,116,137,143,153,189,207,213,245,257,267],"boils":[66],"down":[67],"task":[70],"learning":[72],"optimal":[74,105],"function":[76],"that":[77,148],"achieves":[78],"minimum":[80,132],"Wasserstein":[81,135,160],"distance.":[82],"Specifically,":[83,253],"given":[84],"level":[87],"predictions":[88,190],"ground":[91],"truth":[92],"labels,":[93],"we":[94,108,209],"first":[95],"map":[96],"them":[97],"into":[98],"two":[99,216],"probability":[100,111],"vectors.":[101],"Analogous":[102],"transport":[106],"problem,":[107],"view":[109],"each":[110],"vector":[112],"pile":[115,142,152],"relevance":[117,145,155,183,196],"mass":[118,146],"peaks":[120],"indicating":[121],"higher":[122],"relevance.":[123],"The":[124,157,219],"loss":[127],"is":[128,201],"formulated":[129],"cost":[133,199],"(the":[134],"distance)":[136],"transporting":[138],"(or":[139],"reshaping)":[140],"predicted":[144],"so":[147],"it":[149],"matches":[150],"ground-truth":[154],"mass.":[156],"smaller":[158],"distance":[161],"is,":[162],"closer":[164],"prediction":[166],"gets":[167],"ground-truth.":[170],"To":[171,203],"better":[172],"capture":[173],"inherent":[175],"relevance-based":[176],"order":[177],"information":[178],"among":[179],"different":[182,250],"labels":[184],"lower":[186],"variance":[188],"for":[191],"label,":[197],"ranking-specific":[198],"matrix":[200],"imposed.":[202],"validate":[204],"effectiveness":[206],"conduct":[210],"series":[212],"experiments":[214],"benchmark":[217],"collections.":[218],"experimental":[220],"results":[221],"demonstrate":[222],"that:":[223],"compared":[224],"four":[226],"non-trivial":[227],"methods":[230],"(i.e.,":[231],"LambdaRank,":[232,260],"ListNet,":[233,261],"ListMLE":[234,262],"ApxNDCG),":[236],"WassRank":[237,258],"can":[238],"achieve":[239],"substantially":[240],"improved":[241],"performance":[242],"terms":[244,266],"nDCG":[246],"ERR":[248],"across":[249],"positions.":[252],"maximum":[255],"improvements":[256],"over":[259],"ApxNDCG":[264],"[email":[268],"protected]":[269],"15%,":[271],"5%,":[272,274],"7%,":[273],"respectively.":[275]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-01-11T00:00:00"}
