{"id":"https://openalex.org/W4220718137","doi":"https://doi.org/10.47989/irpaper920","title":"Correlation and prediction of high-cost information retrieval evaluation metrics using deep learning","display_name":"Correlation and prediction of high-cost information retrieval evaluation metrics using deep learning","publication_year":2022,"publication_date":"2022-03-12","ids":{"openalex":"https://openalex.org/W4220718137","doi":"https://doi.org/10.47989/irpaper920"},"language":"en","primary_location":{"id":"doi:10.47989/irpaper920","is_oa":true,"landing_page_url":"https://doi.org/10.47989/irpaper920","pdf_url":null,"source":{"id":"https://openalex.org/S4210195660","display_name":"Information Research an international electronic journal","issn_l":"1368-1613","issn":["1368-1613"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310317106","host_organization_name":"University of Bor\u00e5s","host_organization_lineage":["https://openalex.org/P4310317106"],"host_organization_lineage_names":["University of Bor\u00e5s"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Research: an international electronic journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.47989/irpaper920","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057642496","display_name":"Sinyinda Muwanei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sinyinda Muwanei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007931408","display_name":"Sri Devi Ravana","orcid":"https://orcid.org/0000-0002-5637-9158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sri Devi Ravana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091792637","display_name":"Wai Lam Hoo","orcid":"https://orcid.org/0009-0006-2540-8020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wai Lam Hoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054606411","display_name":"Douglas Kunda","orcid":"https://orcid.org/0000-0002-1146-1975"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Douglas Kunda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081983920","display_name":"Prabha Rajagopal","orcid":"https://orcid.org/0000-0002-5734-3691"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prabha Rajagopal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039750599","display_name":"Prabhpreet Singh Sodhi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prabhpreet Singh Sodhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5057642496"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3186,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58295989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"27","issue":"1","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":0.9994999766349792,"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":0.9994999766349792,"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":0.9976000189781189,"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/T11719","display_name":"Data Quality and Management","score":0.9950000047683716,"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/computer-science","display_name":"Computer science","score":0.7184763550758362},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6619467735290527},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.6172163486480713},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5564607977867126},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.50774085521698},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5026063919067383},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.49077877402305603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48354148864746094},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4808405339717865},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4728572368621826},{"id":"https://openalex.org/keywords/rank-correlation","display_name":"Rank correlation","score":0.4704774022102356},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.46137604117393494},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.43556341528892517},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42647019028663635},{"id":"https://openalex.org/keywords/evaluation-methods","display_name":"Evaluation methods","score":0.41987860202789307},{"id":"https://openalex.org/keywords/spearmans-rank-correlation-coefficient","display_name":"Spearman's rank correlation coefficient","score":0.4164174497127533},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23423248529434204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18233704566955566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7184763550758362},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6619467735290527},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6172163486480713},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5564607977867126},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.50774085521698},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5026063919067383},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.49077877402305603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48354148864746094},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4808405339717865},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4728572368621826},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.4704774022102356},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.46137604117393494},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.43556341528892517},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42647019028663635},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.41987860202789307},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.4164174497127533},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23423248529434204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18233704566955566},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.47989/irpaper920","is_oa":true,"landing_page_url":"https://doi.org/10.47989/irpaper920","pdf_url":null,"source":{"id":"https://openalex.org/S4210195660","display_name":"Information Research an international electronic journal","issn_l":"1368-1613","issn":["1368-1613"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310317106","host_organization_name":"University of Bor\u00e5s","host_organization_lineage":["https://openalex.org/P4310317106"],"host_organization_lineage_names":["University of Bor\u00e5s"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Research: an international electronic journal","raw_type":"journal-article"},{"id":"pmh:oai:monash.edu:openaire/e9f575eb-ccc1-448a-9759-661490b69ab2","is_oa":false,"landing_page_url":"https://research.monash.edu/en/publications/e9f575eb-ccc1-448a-9759-661490b69ab2","pdf_url":null,"source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Muwanei, S, Ravana, S D, Hoo, W L, Kunda, D, Rajagopal, P & Sodhi, P S 2022, 'Correlation and prediction of high-cost information retrieval evaluation metrics using deep learning', Information Research, vol. 27, no. 1, 920. https://doi.org/10.47989/irpaper920","raw_type":"article"},{"id":"pmh:oai:monash.edu:publications/e9f575eb-ccc1-448a-9759-661490b69ab2","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85137689995&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Muwanei , S , Ravana , S D , Hoo , W L , Kunda , D , Rajagopal , P &amp; Sodhi , P S 2022 , ' Correlation and prediction of high-cost information retrieval evaluation metrics using deep learning ' , Information Research , vol. 27 , no. 1 , 920 . https://doi.org/10.47989/irpaper920","raw_type":"article"}],"best_oa_location":{"id":"doi:10.47989/irpaper920","is_oa":true,"landing_page_url":"https://doi.org/10.47989/irpaper920","pdf_url":null,"source":{"id":"https://openalex.org/S4210195660","display_name":"Information Research an international electronic journal","issn_l":"1368-1613","issn":["1368-1613"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310317106","host_organization_name":"University of Bor\u00e5s","host_organization_lineage":["https://openalex.org/P4310317106"],"host_organization_lineage_names":["University of Bor\u00e5s"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Research: an international electronic journal","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W75771211","https://openalex.org/W95791645","https://openalex.org/W159389352","https://openalex.org/W193708675","https://openalex.org/W195021882","https://openalex.org/W1483624827","https://openalex.org/W1509991416","https://openalex.org/W1532325895","https://openalex.org/W1968927634","https://openalex.org/W1970318294","https://openalex.org/W1976076261","https://openalex.org/W1983595289","https://openalex.org/W1988091129","https://openalex.org/W1990170776","https://openalex.org/W2008445237","https://openalex.org/W2015338694","https://openalex.org/W2021856948","https://openalex.org/W2031536661","https://openalex.org/W2046118959","https://openalex.org/W2053100920","https://openalex.org/W2057495142","https://openalex.org/W2066273153","https://openalex.org/W2068333205","https://openalex.org/W2069870183","https://openalex.org/W2075893676","https://openalex.org/W2078729846","https://openalex.org/W2091524854","https://openalex.org/W2109244020","https://openalex.org/W2110202502","https://openalex.org/W2113640060","https://openalex.org/W2124504084","https://openalex.org/W2141175968","https://openalex.org/W2149730913","https://openalex.org/W2154767044","https://openalex.org/W2468776465","https://openalex.org/W2609795610","https://openalex.org/W2756718824","https://openalex.org/W2758898480","https://openalex.org/W2794432940","https://openalex.org/W2894728481","https://openalex.org/W2913639701","https://openalex.org/W2932505619","https://openalex.org/W2944053130","https://openalex.org/W3016935865","https://openalex.org/W4252577790"],"related_works":["https://openalex.org/W3152154711","https://openalex.org/W2999441357","https://openalex.org/W1911095394","https://openalex.org/W2773390159","https://openalex.org/W2749680699","https://openalex.org/W2357941858","https://openalex.org/W2358850878","https://openalex.org/W2555516226","https://openalex.org/W2963320501","https://openalex.org/W2389190814"],"abstract_inverted_index":{"Introduction.":[0],"To":[1],"reduce":[2],"cost":[3],"of":[4,7,33,46,54,66,96,108,123,192,203,207],"the":[5,22,43,56,67,94,97,112,136,154,175,184,187,201,204],"evaluation":[6,24,35,76,98,205],"information":[8,208],"retrieval":[9,148,193,209],"systems,":[10],"this":[11],"study":[12,133],"proposes":[13],"a":[14],"method":[15,115,138,182],"that":[16,135],"employs":[17,116],"deep":[18,117],"learning":[19],"to":[20,29,51,91,198],"predict":[21],"precision":[23,158,162,169],"metric.":[25],"It":[26,49,150],"also":[27,151],"aims":[28,50],"show":[30,73],"why":[31,74,153,165],"some":[32,75,190],"existing":[34],"metrics":[36,77,99,170,194,206],"correlate":[37,78],"with":[38,79,106],"each":[39,80],"other":[40,143],"while":[41],"considering":[42],"varying":[44],"distributions":[45],"relevance":[47,109],"assessments.":[48,110],"ensure":[52],"reproducibility":[53],"all":[55],"presented":[57],"experiments.":[58],"Method.":[59],"Using":[60],"data":[61],"from":[62],"several":[63],"test":[64],"collections":[65],"Text":[68],"REetrieval":[69],"Conference":[70],"(TREC)":[71],"we":[72],"other,":[81],"through":[82],"mathematical":[83],"intuitions.":[84],"In":[85],"addition,":[86],"regression":[87],"models":[88],"were":[89],"used":[90],"investigate":[92],"how":[93],"predictions":[95,141,202],"are":[100],"affected":[101],"by":[102],"queries":[103],"or":[104],"topics":[105],"variations":[107],"Lastly,":[111],"proposed":[113,137,145,181],"prediction":[114],"learning.":[118],"Analysis.":[119],"We":[120],"use":[121],"coefficient":[122],"determination,":[124],"Kendall's":[125],"tau,":[126],"Spearman":[127],"and":[128,159,164,167,183],"Pearson":[129],"correlations.":[130],"Results.":[131],"This":[132],"showed":[134,152],"performed":[139],"better":[140],"than":[142],"recently":[144],"methods":[146],"in":[147],"research.":[149],"correlation":[155,173],"exists":[156],"between":[157,189],"rank":[160],"biased":[161],"metrics,":[163],"recall":[166],"average":[168],"have":[171],"reduced":[172],"when":[174],"cut-off":[176],"depth":[177],"increases.":[178],"Conclusions.":[179],"The":[180],"justifications":[185],"for":[186,200],"correlations":[188],"pairs":[191],"will":[195],"be":[196],"valuable":[197],"researchers":[199],"systems.":[210]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
