{"id":"https://openalex.org/W2216964862","doi":"https://doi.org/10.1145/2736277.2741668","title":"Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks","display_name":"Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks","publication_year":2015,"publication_date":"2015-05-18","ids":{"openalex":"https://openalex.org/W2216964862","doi":"https://doi.org/10.1145/2736277.2741668","mag":"2216964862"},"language":"en","primary_location":{"id":"doi:10.1145/2736277.2741668","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2736277.2741668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","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/A5110629518","display_name":"Joseph J. Pfeiffer","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Joseph J. Pfeiffer","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064439579","display_name":"Jennifer Neville","orcid":"https://orcid.org/0000-0001-8108-4899"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Neville","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102869952","display_name":"Paul N. Bennett","orcid":"https://orcid.org/0000-0002-8846-5480"},"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":"Paul N. Bennett","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110629518"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":5.7862,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.96196505,"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":"853","last_page":"863"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9994999766349792,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9994999766349792,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9873999953269958,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9872000217437744,"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/exploit","display_name":"Exploit","score":0.7879235744476318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7871794700622559},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6542927026748657},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.6333351731300354},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.6313539147377014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5520428419113159},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5447351932525635},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.42702335119247437},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4259895086288452},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.3940659165382385},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31786757707595825}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7879235744476318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7871794700622559},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6542927026748657},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.6333351731300354},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.6313539147377014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5520428419113159},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5447351932525635},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.42702335119247437},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4259895086288452},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3940659165382385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31786757707595825},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/2736277.2741668","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2736277.2741668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.695.3415","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.695.3415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.www2015.it/documents/proceedings/proceedings/p853.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.723.455","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.723.455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.cs.purdue.edu/homes/jpfeiff/pubs/WWW2015_MaxEntInf.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4"}],"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":22,"referenced_works":["https://openalex.org/W1497163089","https://openalex.org/W1585529040","https://openalex.org/W1860880244","https://openalex.org/W1977970897","https://openalex.org/W1994473607","https://openalex.org/W2028660080","https://openalex.org/W2045012549","https://openalex.org/W2087743293","https://openalex.org/W2097089247","https://openalex.org/W2111708605","https://openalex.org/W2118932399","https://openalex.org/W2124298085","https://openalex.org/W2139823104","https://openalex.org/W2141793026","https://openalex.org/W2144211451","https://openalex.org/W2167411378","https://openalex.org/W2168947765","https://openalex.org/W2251114547","https://openalex.org/W2604272474","https://openalex.org/W2952900479","https://openalex.org/W2962735828","https://openalex.org/W4399584694"],"related_works":["https://openalex.org/W3181676408","https://openalex.org/W3021676282","https://openalex.org/W1549959306","https://openalex.org/W3008176773","https://openalex.org/W320292658","https://openalex.org/W4313639514","https://openalex.org/W2186138942","https://openalex.org/W3186228248","https://openalex.org/W2806326686","https://openalex.org/W2001007279"],"abstract_inverted_index":{"Many":[0],"individuals":[1],"on":[2],"social":[3],"networking":[4,16],"sites":[5,17],"provide":[6,23],"traits":[7],"about":[8],"themselves,":[9],"such":[10,31],"as":[11,32,64],"interests":[12],"or":[13,36],"demographics.":[14],"Social":[15],"can":[18],"use":[19],"this":[20],"information":[21],"to":[22,26,46,51,86],"better":[24],"content":[25],"match":[27],"their":[28,72],"users'":[29],"interests,":[30],"recommending":[33],"scheduled":[34],"events":[35],"various":[37],"relevant":[38],"products.":[39],"These":[40],"tasks":[41],"require":[42],"accurate":[43],"probability":[44],"estimates":[45],"determine":[47],"the":[48,68,75,88],"correct":[49],"answer":[50],"return.":[52],"Relational":[53],"machine":[54],"learning":[55,80],"(RML)":[56],"is":[57],"an":[58],"excellent":[59],"framework":[60],"for":[61],"these":[62],"problems":[63],"it":[65],"jointly":[66],"models":[67],"user":[69],"labels":[70],"given":[71],"attributes":[73],"and":[74],"relational":[76],"structure.":[77],"Further,":[78],"semi-supervised":[79],"methods":[81,85],"could":[82],"enable":[83],"RML":[84],"exploit":[87],"large":[89],"amount":[90],"of":[91],"unlabeled":[92],"data":[93],"in":[94],"networks.":[95]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
