{"id":"https://openalex.org/W2119915772","doi":"https://doi.org/10.1145/2348283.2348410","title":"Confidence-aware graph regularization with heterogeneous pairwise features","display_name":"Confidence-aware graph regularization with heterogeneous pairwise features","publication_year":2012,"publication_date":"2012-08-12","ids":{"openalex":"https://openalex.org/W2119915772","doi":"https://doi.org/10.1145/2348283.2348410","mag":"2119915772"},"language":"en","primary_location":{"id":"doi:10.1145/2348283.2348410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2348283.2348410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ink.library.smu.edu.sg/sis_research/4061","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055103025","display_name":"Yuan Fang","orcid":"https://orcid.org/0000-0002-4265-5289"},"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":"Yuan Fang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA","University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081017604","display_name":"Bo-June Hsu","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":"Bo-June (Paul) Hsu","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101880377","display_name":"Kevin Chen\u2013Chuan Chang","orcid":"https://orcid.org/0000-0003-0997-6803"},"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":"Kevin Chen-Chuan Chang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA","University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055103025"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.326,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8446753,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"951","last_page":"960"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9921000003814697,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.9144870638847351},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.8024202585220337},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7790573239326477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7434824109077454},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5196586847305298},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5138826370239258},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.482101708650589},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.42752858996391296},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4104050397872925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36300867795944214},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23745504021644592},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.136073499917984}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.9144870638847351},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.8024202585220337},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7790573239326477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7434824109077454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5196586847305298},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5138826370239258},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.482101708650589},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.42752858996391296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4104050397872925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36300867795944214},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23745504021644592},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.136073499917984},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2348283.2348410","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2348283.2348410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-5064","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/sis_research/4061","pdf_url":null,"source":{"id":"https://openalex.org/S4306401925","display_name":"Singapore Management University Institutional Knowledge (InK) (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"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":"https://doi.org/10.1145/2348283.2348410","raw_type":"Conference Proceeding Article"}],"best_oa_location":{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-5064","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/sis_research/4061","pdf_url":null,"source":{"id":"https://openalex.org/S4306401925","display_name":"Singapore Management University Institutional Knowledge (InK) (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"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":"https://doi.org/10.1145/2348283.2348410","raw_type":"Conference Proceeding Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1491300635","https://openalex.org/W1999322447","https://openalex.org/W2034260175","https://openalex.org/W2034927834","https://openalex.org/W2085897424","https://openalex.org/W2090916616","https://openalex.org/W2098876286","https://openalex.org/W2104290444","https://openalex.org/W2122837498","https://openalex.org/W2139823104","https://openalex.org/W2141880913","https://openalex.org/W2143956411","https://openalex.org/W2146446188","https://openalex.org/W2146728697","https://openalex.org/W2154455818","https://openalex.org/W2155540986","https://openalex.org/W2160555926","https://openalex.org/W2162963578","https://openalex.org/W2163375626","https://openalex.org/W2341283081","https://openalex.org/W2997701990","https://openalex.org/W6681689991","https://openalex.org/W6682494755"],"related_works":["https://openalex.org/W1496222301","https://openalex.org/W3207760230","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W41015297","https://openalex.org/W4280645561"],"abstract_inverted_index":{"Conventional":[0],"classification":[1,86],"methods":[2],"tend":[3],"to":[4,78],"focus":[5],"on":[6,14,28,145,165],"features":[7,18,55,123],"of":[8,42,120,138,148,158,162],"individual":[9],"objects,":[10,98],"while":[11],"missing":[12,101],"out":[13],"potentially":[15],"valuable":[16],"pairwise":[17,43,54,122],"that":[19,51,116],"capture":[20],"the":[21,68,118,146,160],"relationships":[22,69],"between":[23,70],"objects.":[24,71],"Although":[25],"recent":[26],"developments":[27],"graph":[29,104],"regularization":[30,105,114],"exploit":[31],"this":[32,108],"aspect,":[33],"existing":[34],"works":[35],"generally":[36,63],"assume":[37],"only":[38],"a":[39,112,135,156,166],"single":[40],"kind":[41],"feature,":[44],"which":[45],"is":[46],"often":[47,57],"insufficient.":[48],"We":[49],"observe":[50],"multiple,":[52],"heterogeneous":[53,121],"can":[56],"complement":[58],"each":[59],"other":[60],"and":[61],"are":[62,76],"more":[64,91,96],"robust":[65],"in":[66,141],"modeling":[67],"Furthermore,":[72],"as":[73],"some":[74],"objects":[75,82],"easier":[77],"classify":[79],"than":[80],"others,":[81],"with":[83,124],"higher":[84],"initial":[85],"confidence":[87],"should":[88],"be":[89],"weighed":[90],"towards":[92],"classifying":[93],"related":[94],"but":[95],"ambiguous":[97],"an":[99],"observation":[100],"from":[102],"previous":[103],"techniques.":[106],"In":[107],"paper,":[109],"we":[110,133,153],"propose":[111],"Dirichlet-based":[113],"framework":[115,140,164],"supports":[117],"combination":[119],"confidence-aware":[125],"prediction":[126],"using":[127],"limited":[128],"labeled":[129],"training":[130],"data.":[131],"Next,":[132],"showcase":[134],"few":[136],"applications":[137],"our":[139,163],"information":[142],"retrieval,":[143],"focusing":[144],"problem":[147],"query":[149],"intent":[150],"classification.":[151],"Finally,":[152],"demonstrate":[154],"through":[155],"series":[157],"experiments":[159],"advantages":[161],"large-scale":[167],"real-world":[168],"dataset.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
