{"id":"https://openalex.org/W2127564752","doi":"https://doi.org/10.5555/2567709.2567742","title":"Learning bilinear model for matching queries and documents","display_name":"Learning bilinear model for matching queries and documents","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2127564752","doi":"https://doi.org/10.5555/2567709.2567742","mag":"2127564752"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.406.7916","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.7916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume14/wu13a/wu13a.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"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/A5101796417","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0001-6572-8471","observed_orcids":["https://orcid.org/0000-0001-6572-8471"]},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"WuWei","raw_affiliation_strings":["[Microsoft Research Asia, Beijing, P.R. China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[Microsoft Research Asia, Beijing, P.R. China]","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084830556","display_name":"Zhengdong Lu","orcid":"https://orcid.org/0000-0002-6418-6030","observed_orcids":["https://orcid.org/0000-0002-6418-6030"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LuZhengdong","raw_affiliation_strings":["[Huawei Noah's Ark Lab, Shatin, Hong Kong]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[Huawei Noah's Ark Lab, Shatin, Hong Kong]","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455139","display_name":"Hang Li","orcid":"https://orcid.org/0000-0002-6221-6195","observed_orcids":["https://orcid.org/0000-0002-6221-6195","https://orcid.org/0000-0002-7343-3865","https://orcid.org/0009-0008-5689-6383"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LiHang","raw_affiliation_strings":["[Huawei Noah's Ark Lab, Shatin, Hong Kong]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"[Huawei Noah's Ark Lab, Shatin, Hong Kong]","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.9203,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.97789171,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9291999936103821,"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"}},"topics":[{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9291999936103821,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.029400000348687172,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.014600000344216824,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/query-document-matching","display_name":"query-document matching","score":0.8080000281333923},{"id":"https://openalex.org/keywords/bilinear-model","display_name":"bilinear model","score":0.7009999752044678},{"id":"https://openalex.org/keywords/partial-least-squares","display_name":"partial least squares","score":0.7590000033378601},{"id":"https://openalex.org/keywords/l1-and-l2-regularization","display_name":"L1 and L2 regularization","score":0.8109999895095825},{"id":"https://openalex.org/keywords/generalization-analysis","display_name":"generalization analysis","score":0.7509999871253967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8168787956237793},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5851583480834961},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.5149255990982056},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49165278673171997},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.48858389258384705},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.443986713886261},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33427825570106506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3331618309020996},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11130079627037048},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10905981063842773},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.406.7916","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.7916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume14/wu13a/wu13a.pdf","raw_type":"text"},{"id":"mag:3086167277","doi":"https://doi.org/10.5555/2567709.2567742","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/10.5555/2567709.2567742","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W21207210","https://openalex.org/W186258178","https://openalex.org/W1482214997","https://openalex.org/W1502981577","https://openalex.org/W1534694010","https://openalex.org/W1646506067","https://openalex.org/W1678356000","https://openalex.org/W1733921475","https://openalex.org/W1956559956","https://openalex.org/W1964514974","https://openalex.org/W1967435720","https://openalex.org/W1972594981","https://openalex.org/W1973435495","https://openalex.org/W1985554184","https://openalex.org/W2009077327","https://openalex.org/W2019263612","https://openalex.org/W2031823405","https://openalex.org/W2047221353","https://openalex.org/W2049455633","https://openalex.org/W2061296752","https://openalex.org/W2086378526","https://openalex.org/W2093390569","https://openalex.org/W2098929588","https://openalex.org/W2100235303","https://openalex.org/W2103971611","https://openalex.org/W2106053110","https://openalex.org/W2108862644","https://openalex.org/W2109531142","https://openalex.org/W2109670852","https://openalex.org/W2116413942","https://openalex.org/W2117154949","https://openalex.org/W2118393783","https://openalex.org/W2121033924","https://openalex.org/W2122090912","https://openalex.org/W2125398996","https://openalex.org/W2127069950","https://openalex.org/W2129921015","https://openalex.org/W2137225583","https://openalex.org/W2137736727","https://openalex.org/W2139688392","https://openalex.org/W2142345475","https://openalex.org/W2142387771","https://openalex.org/W2142537246","https://openalex.org/W2147152072","https://openalex.org/W2160555926","https://openalex.org/W2162867699","https://openalex.org/W2169495281","https://openalex.org/W2170356051","https://openalex.org/W2171456302","https://openalex.org/W2171541062","https://openalex.org/W2579923771","https://openalex.org/W2988119488","https://openalex.org/W3099514962"],"related_works":["https://openalex.org/W110977595","https://openalex.org/W4285750439","https://openalex.org/W2105151169","https://openalex.org/W2336509759","https://openalex.org/W2154370935","https://openalex.org/W7098724797","https://openalex.org/W2100247763","https://openalex.org/W3004147752","https://openalex.org/W7101053201","https://openalex.org/W3150359542"],"abstract_inverted_index":{"The":[0,89,233],"task":[1],"of":[2,44,51,106,123,213,223],"matching":[3,72,124,177],"data":[4],"from":[5],"two":[6,92,96],"heterogeneous":[7,87],"domains":[8],"naturally":[9],"arises":[10],"in":[11],"various":[12],"areas":[13],"such":[14],"as":[15,139],"web":[16,24,197,230],"search,":[17,25],"collaborative":[18],"filtering,":[19],"and":[20,35,46,67,99,170,183,190,199,203,215,221,225,239],"drug":[21],"design.":[22],"In":[23,76],"existing":[26],"work":[27,59],"has":[28,56],"designed":[29],"relevance":[30],"models":[31],"to":[32,63,69,85,156,187,196],"match":[33,86],"queries":[34,45,214],"documents":[36],"by":[37],"exploiting":[38],"either":[39],"user":[40],"clicks":[41,66],"or":[42],"content":[43,68],"documents.":[47,216],"To":[48,173],"the":[49,103,117,132,176,185,194,219,251],"best":[50],"our":[52],"knowledge,":[53],"however,":[54],"there":[55],"been":[57],"little":[58],"on":[60,129,227],"principled":[61],"approaches":[62],"leveraging":[64],"both":[65,188,201,237],"learn":[70],"a":[71,81,120,140,145,150,206],"model":[73,152],"for":[74,83,95],"search.":[75],"this":[77],"paper,":[78],"we":[79,148,179],"propose":[80],"framework":[82,90,118,133,195],"learning":[84,252],"objects.":[88],"learns":[91],"linear":[93],"mappings":[94],"objects":[97],"respectively,":[98],"matches":[100],"them":[101],"via":[102],"dot":[104],"product":[105],"their":[107],"images":[108],"after":[109],"mapping.":[110],"Moreover,":[111],"when":[112],"different":[113],"regularizations":[114],"are":[115],"enforced,":[116],"renders":[119],"rich":[121],"family":[122],"models.":[125],"With":[126],"orthonormal":[127],"constraints":[128],"mapping":[130],"functions,":[131],"subsumes":[134],"Partial":[135],"Least":[136],"Squares":[137],"(PLS)":[138],"special":[141],"case.":[142],"Alternatively,":[143],"with":[144,209],"\u21131+\u21132":[146],"regularization,":[147],"obtain":[149],"new":[151],"called":[153],"Regularized":[154],"Mapping":[155],"Latent":[157],"Structures":[158],"(RMLS).":[159],"RMLS":[160,204,224,240,247],"enjoys":[161],"many":[162],"advantages":[163],"over":[164],"PLS,":[165],"including":[166],"lower":[167],"time":[168],"complexity":[169],"easy":[171],"parallelization.":[172],"further":[174],"understand":[175],"framework,":[178],"conduct":[180],"generalization":[181],"analysis":[182],"apply":[184,193],"result":[186],"PLS":[189,202,226,238],"RMLS.":[191],"We":[192,217],"search":[198,231],"implement":[200],"using":[205],"click-through":[207],"bipartite":[208],"metadata":[210],"representing":[211],"features":[212],"test":[218],"efficacy":[220],"scalability":[222],"large":[228],"scale":[229],"problems.":[232],"results":[234],"show":[235],"that":[236],"can":[241],"significantly":[242],"outperform":[243],"baseline":[244],"methods,":[245],"while":[246],"substantially":[248],"speeds":[249],"up":[250],"process.":[253]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":1}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2016-06-24T00:00:00"}