{"id":"https://openalex.org/W2134658893","doi":"https://doi.org/10.1145/1871437.1871567","title":"Probabilistic first pass retrieval for search advertising","display_name":"Probabilistic first pass retrieval for search advertising","publication_year":2010,"publication_date":"2010-10-26","ids":{"openalex":"https://openalex.org/W2134658893","doi":"https://doi.org/10.1145/1871437.1871567","mag":"2134658893"},"language":"en","primary_location":{"id":"doi:10.1145/1871437.1871567","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1871437.1871567","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM international conference on Information and knowledge management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5028621953","display_name":"Hema Raghavan","orcid":"https://orcid.org/0000-0002-8011-8767"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]},{"id":"https://openalex.org/I1325784139","display_name":"Yahoo (United Kingdom)","ror":"https://ror.org/038p3gq39","country_code":"GB","type":"company","lineage":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"]}],"countries":["GB","US"],"is_corresponding":true,"raw_author_name":"Hema Raghavan","raw_affiliation_strings":["Yahoo! Inc, Santa Clara, CA, USA","[Yahoo, Inc., Santa Clara, CA, USA]"],"affiliations":[{"raw_affiliation_string":"Yahoo! Inc, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"[Yahoo, Inc., Santa Clara, CA, USA]","institution_ids":["https://openalex.org/I1325784139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112460373","display_name":"Rukmini Iyer","orcid":null},"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":"Rukmini Iyer","raw_affiliation_strings":["Microsoft Corp, Mountain View, CA, USA","Microsoft Corporation, Mountain View, CA, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Microsoft Corp, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Corporation, Mountain View, CA, USA#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028621953"],"corresponding_institution_ids":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"],"apc_list":null,"apc_paid":null,"fwci":2.0811,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.91282711,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1019","last_page":"1028"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9984999895095825,"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":0.9984999895095825,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9980999827384949,"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":0.9969000220298767,"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":0.8773836493492126},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.7086476683616638},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6316923499107361},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.5939391255378723},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5819862484931946},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5433465242385864},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5399719476699829},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5375845432281494},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.5215904116630554},{"id":"https://openalex.org/keywords/vector-space-model","display_name":"Vector space model","score":0.4881795048713684},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4683030843734741},{"id":"https://openalex.org/keywords/relevance-feedback","display_name":"Relevance feedback","score":0.4508334696292877},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.43710044026374817},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.42977339029312134},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4252042770385742},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25880080461502075},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.1804172396659851}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8773836493492126},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.7086476683616638},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6316923499107361},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.5939391255378723},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5819862484931946},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5433465242385864},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5399719476699829},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5375845432281494},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.5215904116630554},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.4881795048713684},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4683030843734741},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.4508334696292877},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.43710044026374817},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.42977339029312134},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4252042770385742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25880080461502075},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.1804172396659851},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1871437.1871567","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1871437.1871567","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM international conference on Information and knowledge management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1646006088","https://openalex.org/W1863141190","https://openalex.org/W1964348731","https://openalex.org/W1967766192","https://openalex.org/W1972594981","https://openalex.org/W2002306339","https://openalex.org/W2009892466","https://openalex.org/W2048045485","https://openalex.org/W2053135957","https://openalex.org/W2062270497","https://openalex.org/W2066395238","https://openalex.org/W2069870183","https://openalex.org/W2070740689","https://openalex.org/W2090883204","https://openalex.org/W2093390569","https://openalex.org/W2098876286","https://openalex.org/W2108079923","https://openalex.org/W2113227443","https://openalex.org/W2116030947","https://openalex.org/W2123198781","https://openalex.org/W2138662031","https://openalex.org/W2139273454","https://openalex.org/W2142589616","https://openalex.org/W2146446188","https://openalex.org/W2154610494","https://openalex.org/W2156577800","https://openalex.org/W2161563551","https://openalex.org/W2165612380","https://openalex.org/W2169213601","https://openalex.org/W2170741935","https://openalex.org/W2423725643","https://openalex.org/W3122305203","https://openalex.org/W4206765718","https://openalex.org/W4242239371","https://openalex.org/W4245107743","https://openalex.org/W4245350944","https://openalex.org/W4246858749"],"related_works":["https://openalex.org/W4234076403","https://openalex.org/W2136177730","https://openalex.org/W2576473474","https://openalex.org/W2572349046","https://openalex.org/W2382153208","https://openalex.org/W1160915619","https://openalex.org/W2027155619","https://openalex.org/W2577784223","https://openalex.org/W2364053392","https://openalex.org/W1561049396"],"abstract_inverted_index":{"Information":[0],"retrieval":[1,9,68],"in":[2,6,47,63,100,120,182,192,198,209,211,217],"search":[3,54,73,157],"advertising,":[4],"as":[5,188],"other":[7],"ad-hoc":[8],"tasks,":[10],"aims":[11],"to":[12,31,39,50,114,169],"find":[13],"the":[14,19,33,48,77,91,103,121,166,231,237],"most":[15],"appropriate":[16],"ranking":[17,32],"of":[18,22,126,136,154,184,239,246],"ad":[20,34,67],"documents":[21],"a":[23,26,65,71,106,111,155,241],"corpus":[24],"for":[25,70,82,129],"given":[27],"query.":[28],"In":[29,56,174],"addition":[30],"documents,":[35],"we":[36,59,109,148,161,225],"also":[37,149,205],"need":[38],"filter":[40],"or":[41],"threshold":[42],"irrelevant":[43],"ads":[44],"from":[45],"participating":[46],"auction":[49],"be":[51],"displayed":[52],"alongside":[53],"results.":[55],"this":[57,228],"work,":[58],"describe":[60],"our":[61,176],"experience":[62],"implementing":[64],"successful":[66],"system":[69,97,242],"commercial":[72],"engine":[74,158],"based":[75],"on":[76,201],"Language":[78],"Modeling":[79],"(LM)":[80],"framework":[81],"retrieval.":[83],"The":[84],"LM":[85,122,177],"demonstrates":[86,206],"significant":[87,207],"performance":[88],"improvements":[89,208],"over":[90],"baseline":[92],"vector":[93],"space":[94],"model":[95],"(TF-IDF)":[96],"that":[98,227,243],"was":[99],"production":[101,156],"at":[102],"time.":[104],"From":[105,144],"modeling":[107],"perspective,":[108,147],"propose":[110],"novel":[112],"approach":[113],"incorporate":[115],"query":[116,138,141],"segmentation":[117],"and":[118,132,159,194,204],"phrases":[119],"framework,":[123],"discuss":[124,150],"impact":[125],"score":[127],"normalization":[128],"relevance":[130],"filtering,":[131],"present":[133],"preliminary":[134],"results":[135],"incorporating":[137],"expansions":[139],"using":[140],"rewriting":[142],"techniques.":[143],"an":[145],"implementation":[146],"real-time":[151],"latency":[152],"constraints":[153],"how":[160],"overcome":[162],"them":[163],"by":[164],"adapting":[165],"WAND":[167],"algorithm":[168],"work":[170],"with":[171,220,233],"language":[172],"models.":[173],"sum,":[175],"formulation":[178],"is":[179],"considerably":[180],"better":[181],"terms":[183],"accuracy":[185],"metrics":[186],"such":[187],"Precision-Recall":[189],"(10%":[190],"improvement":[191,197,216],"AUC)":[193],"nDCG":[195],"(8%":[196],"[email":[199],"protected])":[200],"editorial":[202],"data":[203],"clicks":[210],"live":[212],"user":[213],"tests":[214],"(0.787%":[215],"Click":[218],"Yield,":[219],"8%":[221],"coverage":[222],"increase).":[223],"Finally,":[224],"hope":[226],"paper":[229],"provides":[230],"reader":[232],"adequate":[234],"insights":[235],"into":[236],"challenges":[238],"building":[240],"serves":[244],"millions":[245],"users":[247],"every":[248],"day.":[249]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}