{"id":"https://openalex.org/W2170610543","doi":"https://doi.org/10.1145/1277741.1277797","title":"A study of Poisson query generation model for information retrieval","display_name":"A study of Poisson query generation model for information retrieval","publication_year":2007,"publication_date":"2007-07-23","ids":{"openalex":"https://openalex.org/W2170610543","doi":"https://doi.org/10.1145/1277741.1277797","mag":"2170610543"},"language":"en","primary_location":{"id":"doi:10.1145/1277741.1277797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1277741.1277797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval","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/A5048955398","display_name":"Qiaozhu Mei","orcid":"https://orcid.org/0000-0002-8640-1942"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qiaozhu Mei","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101716412","display_name":"Hui Fang","orcid":"https://orcid.org/0009-0003-1904-787X"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Fang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028518494","display_name":"ChengXiang Zhai","orcid":"https://orcid.org/0000-0002-6434-3702"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"ChengXiang Zhai","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048955398"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":6.2872,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.9634876,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"319","last_page":"326"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9988999962806702,"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/T11719","display_name":"Data Quality and Management","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.8490524291992188},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.8208216428756714},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7693806886672974},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.6764858961105347},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4541094899177551},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4523152709007263},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4307669401168823},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41873520612716675},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.337779700756073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2986357510089874},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.19543957710266113},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18993410468101501},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.173228919506073}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.8490524291992188},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.8208216428756714},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7693806886672974},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.6764858961105347},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4541094899177551},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4523152709007263},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4307669401168823},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41873520612716675},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.337779700756073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2986357510089874},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.19543957710266113},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18993410468101501},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.173228919506073},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1277741.1277797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1277741.1277797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6600000262260437,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1482214997","https://openalex.org/W1550206324","https://openalex.org/W1592871157","https://openalex.org/W1667165204","https://openalex.org/W1880262756","https://openalex.org/W1904228841","https://openalex.org/W1982449511","https://openalex.org/W2014415866","https://openalex.org/W2027445772","https://openalex.org/W2042980227","https://openalex.org/W2043292582","https://openalex.org/W2068905009","https://openalex.org/W2093390569","https://openalex.org/W2095368471","https://openalex.org/W2107743791","https://openalex.org/W2108903064","https://openalex.org/W2111212948","https://openalex.org/W2113110240","https://openalex.org/W2129971563","https://openalex.org/W2130395434","https://openalex.org/W2131133093","https://openalex.org/W2134557008","https://openalex.org/W2136542423","https://openalex.org/W2139201316","https://openalex.org/W2158195707","https://openalex.org/W2162746367","https://openalex.org/W2169213601","https://openalex.org/W4206765718","https://openalex.org/W4233135949","https://openalex.org/W4238346259","https://openalex.org/W4240913316","https://openalex.org/W4243333943","https://openalex.org/W4246858749","https://openalex.org/W6628905179"],"related_works":["https://openalex.org/W2955220190","https://openalex.org/W2383807498","https://openalex.org/W2045471944","https://openalex.org/W2019359474","https://openalex.org/W1972594981","https://openalex.org/W2360581498","https://openalex.org/W2045966063","https://openalex.org/W2047612970","https://openalex.org/W4247637511","https://openalex.org/W2003151323"],"abstract_inverted_index":{"Many":[0],"variants":[1,109],"of":[2,45,64,82,110],"language":[3],"models":[4,13,48,65,70,83,135],"have":[5],"been":[6],"proposed":[7],"for":[8],"information":[9],"retrieval.":[10],"Most":[11],"existing":[12,68],"are":[14,71],"based":[15,23,28,49,74],"on":[16,24,29,50,75,122],"multinomial":[17,69,144],"distribution":[18],"and":[19,40,66,100,119],"would":[20],"score":[21],"documents":[22],"query":[25,31,46],"likelihood":[26],"computed":[27],"a":[30,42],"generation":[32,47],"probabilistic":[33],"model.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"propose":[39],"study":[41],"new":[43,62,112],"family":[44,63],"Poisson":[51,90,139],"distribution.":[52],"We":[53,86,106],"show":[54,87,130],"that":[55,88,131],"while":[56,132],"in":[57],"their":[58,133],"simplest":[59],"forms,":[60],"the":[61,67,79,89,111,138],"equivalent.":[72],"However,":[73],"different":[76,116],"smoothing":[77,99,117],"methods,":[78,118],"two":[80],"families":[81],"behave":[84],"differently.":[85],"model":[91,113,140,145],"has":[92],"several":[93,108],"advantages,":[94],"including":[95],"naturally":[96],"accommodating":[97],"per-term":[98,147],"modeling":[101],"accurate":[102],"background":[103],"more":[104],"efficiently.":[105],"present":[107],"corresponding":[114],"to":[115],"evaluate":[120],"them":[121],"four":[123],"representative":[124],"TREC":[125],"test":[126],"collections.":[127],"The":[128,149],"results":[129],"basic":[134],"perform":[136,143],"comparably,":[137],"can":[141,151],"out":[142],"with":[146,155],"smoothing.":[148,157],"performance":[150],"be":[152],"further":[153],"improved":[154],"two-stage":[156]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}