{"id":"https://openalex.org/W2539104130","doi":"https://doi.org/10.1145/2983323.2983844","title":"Pseudo-Relevance Feedback Based on Matrix Factorization","display_name":"Pseudo-Relevance Feedback Based on Matrix Factorization","publication_year":2016,"publication_date":"2016-10-24","ids":{"openalex":"https://openalex.org/W2539104130","doi":"https://doi.org/10.1145/2983323.2983844","mag":"2539104130"},"language":"en","primary_location":{"id":"doi:10.1145/2983323.2983844","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983844","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on 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/A5101457713","display_name":"Hamed Zamani","orcid":"https://orcid.org/0000-0002-0800-3340"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hamed Zamani","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069233580","display_name":"Javid Dadashkarimi","orcid":"https://orcid.org/0000-0002-1905-547X"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Javid Dadashkarimi","raw_affiliation_strings":["University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055494428","display_name":"Azadeh Shakery","orcid":"https://orcid.org/0000-0003-1799-8340"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Azadeh Shakery","raw_affiliation_strings":["University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105659698","display_name":"W. Bruce Croft","orcid":"https://orcid.org/0000-0003-2391-9629"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"W. Bruce Croft","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101457713"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":18.5777,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.99160406,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1483","last_page":"1492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9986000061035156,"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":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8216571807861328},{"id":"https://openalex.org/keywords/relevance-feedback","display_name":"Relevance feedback","score":0.7423146367073059},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7068834900856018},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.625586211681366},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6036986708641052},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5600766539573669},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5424621105194092},{"id":"https://openalex.org/keywords/vector-space-model","display_name":"Vector space model","score":0.5218440890312195},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.47850456833839417},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.46992409229278564},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4568236172199249},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4343401789665222},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3748796582221985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23103395104408264},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22598060965538025},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.15528962016105652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8216571807861328},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.7423146367073059},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7068834900856018},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.625586211681366},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6036986708641052},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5600766539573669},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5424621105194092},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.5218440890312195},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.47850456833839417},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.46992409229278564},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4568236172199249},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4343401789665222},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3748796582221985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23103395104408264},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22598060965538025},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.15528962016105652},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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:10.1145/2983323.2983844","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983844","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1479822238","https://openalex.org/W1486317198","https://openalex.org/W1557757161","https://openalex.org/W1565544575","https://openalex.org/W1690919088","https://openalex.org/W1823022514","https://openalex.org/W1902027874","https://openalex.org/W1958077162","https://openalex.org/W1964348731","https://openalex.org/W1966093341","https://openalex.org/W1969949308","https://openalex.org/W1992380306","https://openalex.org/W1993692165","https://openalex.org/W2008886893","https://openalex.org/W2010320682","https://openalex.org/W2020098476","https://openalex.org/W2028080169","https://openalex.org/W2043909051","https://openalex.org/W2049653588","https://openalex.org/W2052088591","https://openalex.org/W2068905009","https://openalex.org/W2076683968","https://openalex.org/W2093390569","https://openalex.org/W2102046030","https://openalex.org/W2107370612","https://openalex.org/W2110096996","https://openalex.org/W2114900318","https://openalex.org/W2117473841","https://openalex.org/W2117829666","https://openalex.org/W2135029798","https://openalex.org/W2135728924","https://openalex.org/W2137769383","https://openalex.org/W2142769902","https://openalex.org/W2147152072","https://openalex.org/W2153068592","https://openalex.org/W2156834477","https://openalex.org/W2159859920","https://openalex.org/W2163382007","https://openalex.org/W2164547069","https://openalex.org/W2169213601","https://openalex.org/W2250539671","https://openalex.org/W2468421329","https://openalex.org/W2510769428","https://openalex.org/W2515351093","https://openalex.org/W2532134734","https://openalex.org/W2536345451","https://openalex.org/W4206765718","https://openalex.org/W4236122429","https://openalex.org/W4243333943","https://openalex.org/W4246858749","https://openalex.org/W4254087122","https://openalex.org/W6661611972","https://openalex.org/W6684151790","https://openalex.org/W7030658168"],"related_works":["https://openalex.org/W4234076403","https://openalex.org/W2052625849","https://openalex.org/W2136177730","https://openalex.org/W2576473474","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":{"In":[0,33,130,196],"information":[1],"retrieval,":[2],"pseudo-relevance":[3],"feedback":[4],"(PRF)":[5],"refers":[6],"to":[7,24,49,107,126],"a":[8,43,51,56,88,97,135,142,149,169,185],"strategy":[9],"for":[10,55,203],"updating":[11],"the":[12,16,30,39,46,64,70,76,80,109,118,152,155,160,165,174,180,213,225,235],"query":[13,58,72,119,153,175],"model":[14,73,176],"using":[15,141,168,179,238],"top":[17,156],"retrieved":[18,157],"documents.":[19],"PRF":[20,40,89],"has":[21],"been":[22],"proven":[23],"be":[25,190],"highly":[26],"effective":[27],"in":[28,69,79,100,117,242],"improving":[29],"retrieval":[31,194,208],"performance.":[32],"this":[34,197,201,243],"paper,":[35,198],"we":[36,85,132,163,199],"look":[37],"at":[38],"task":[41],"as":[42],"recommendation":[44,240],"problem:":[45],"goal":[47],"is":[48,96,106,177,184],"recommend":[50],"number":[52],"of":[53,67,111,237],"terms":[54,68,112],"given":[57],"along":[59],"with":[60,192],"weights,":[61],"such":[62],"that":[63,113,145,188,224],"final":[65],"weights":[66],"updated":[71,178],"better":[74],"reflect":[75],"terms'":[77],"contributions":[78],"query.":[81],"To":[82],"do":[83],"so,":[84],"propose":[86],"RFMF,":[87,131],"framework":[90,187,202,227],"based":[91],"on":[92],"matrix":[93,121,136,167,170],"factorization":[94,122,171],"which":[95],"state-of-the-art":[98],"technique":[99],"collaborative":[101],"recommender":[102],"systems.":[103],"Our":[104],"purpose":[105],"predict":[108,127],"weight":[110,143],"have":[114],"not":[115],"appeared":[116],"and":[120,212],"techniques":[123,241],"are":[124,139],"used":[125,206],"these":[128],"weights.":[129],"first":[133],"create":[134],"whose":[137],"elements":[138],"computed":[140],"function":[144],"shows":[146],"how":[147],"much":[148],"term":[150],"discriminates":[151],"or":[154],"documents":[158],"from":[159],"collection.":[161],"Then,":[162],"re-estimate":[164],"created":[166],"technique.":[172],"Finally,":[173],"re-estimated":[181],"matrix.":[182],"RFMF":[183,226],"general":[186],"can":[189],"employed":[191],"any":[193],"model.":[195,216],"implement":[200],"two":[204],"widely":[205],"document":[207],"frameworks:":[209],"language":[210],"modeling":[211],"vector":[214],"space":[215],"Extensive":[217],"experiments":[218],"over":[219],"several":[220],"TREC":[221],"collections":[222],"demonstrate":[223],"significantly":[228],"outperforms":[229],"competitive":[230],"baselines.":[231],"These":[232],"results":[233],"indicate":[234],"potential":[236],"other":[239],"task.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}