{"id":"https://openalex.org/W2616861068","doi":"https://doi.org/10.24963/ijcai.2017/530","title":"Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach","display_name":"Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2616861068","doi":"https://doi.org/10.24963/ijcai.2017/530","mag":"2616861068"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2017/530","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/530","pdf_url":"https://www.ijcai.org/proceedings/2017/0530.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2017/0530.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060117134","display_name":"Lahari Poddar","orcid":"https://orcid.org/0000-0002-5412-7459"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Lahari Poddar","raw_affiliation_strings":["National University of Singapore","School of Computing, National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"School of Computing, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051209739","display_name":"Wynne Hsu","orcid":"https://orcid.org/0000-0002-4142-8893"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wynne Hsu","raw_affiliation_strings":["National University of Singapore","School of Computing, National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"School of Computing, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019988958","display_name":"Mong Li Lee","orcid":"https://orcid.org/0000-0002-9636-388X"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Mong Li Lee","raw_affiliation_strings":["National University of Singapore","School of Computing, National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"School of Computing, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":0.4336,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58657107,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3791","last_page":"3797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9975000023841858,"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":0.9975000023841858,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9884999990463257,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/categorical-variable","display_name":"Categorical variable","score":0.8265985250473022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.657284140586853},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.57569819688797},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5329590439796448},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.514528214931488},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.5099140405654907},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5048962235450745},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5039471983909607},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.49434518814086914},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4923516511917114},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.47917452454566956},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4323791265487671},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.42143112421035767},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35633161664009094}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8265985250473022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657284140586853},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.57569819688797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5329590439796448},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.514528214931488},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.5099140405654907},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5048962235450745},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5039471983909607},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.49434518814086914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4923516511917114},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.47917452454566956},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4323791265487671},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.42143112421035767},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35633161664009094},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.24963/ijcai.2017/530","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/530","pdf_url":"https://www.ijcai.org/proceedings/2017/0530.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1705.05098","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1705.05098","pdf_url":"https://arxiv.org/pdf/1705.05098","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2616861068","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1705.05098","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1705.05098","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1705.05098","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2017/530","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/530","pdf_url":"https://www.ijcai.org/proceedings/2017/0530.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2616861068.pdf","grobid_xml":"https://content.openalex.org/works/W2616861068.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1524701945","https://openalex.org/W1839759725","https://openalex.org/W1994389483","https://openalex.org/W2013652217","https://openalex.org/W2018605660","https://openalex.org/W2019207508","https://openalex.org/W2051779164","https://openalex.org/W2085040216","https://openalex.org/W2099878672","https://openalex.org/W2108306139","https://openalex.org/W2109992782","https://openalex.org/W2132708887","https://openalex.org/W2135183808","https://openalex.org/W2137245235","https://openalex.org/W2151052953","https://openalex.org/W2151383095","https://openalex.org/W2161672051","https://openalex.org/W2171357718","https://openalex.org/W2187922941","https://openalex.org/W3143596294"],"related_works":["https://openalex.org/W2963191516","https://openalex.org/W1970870846","https://openalex.org/W2785448941","https://openalex.org/W2187414160","https://openalex.org/W2004360025","https://openalex.org/W2613140838","https://openalex.org/W326117557","https://openalex.org/W2161040768","https://openalex.org/W3144854221","https://openalex.org/W3143498610","https://openalex.org/W1839759725","https://openalex.org/W37392371","https://openalex.org/W3091295361","https://openalex.org/W2562419692","https://openalex.org/W2339396227","https://openalex.org/W2071415761","https://openalex.org/W2773373314","https://openalex.org/W2367194664","https://openalex.org/W2112084709","https://openalex.org/W2156773750"],"abstract_inverted_index":{"User":[0],"opinions":[1],"expressed":[2],"in":[3,132],"the":[4,17,35,38,55,84,97,123],"form":[5],"of":[6,13,20,46,54,71,126],"ratings":[7,37,58,77],"can":[8],"influence":[9],"an":[10,14,21,47,72],"individual's":[11],"view":[12],"item.":[15,48,73],"However,":[16],"true":[18],"quality":[19,70,145],"item":[22],"is":[23,31],"often":[24],"obfuscated":[25],"by":[26,89],"user":[27,135],"biases,":[28],"and":[29,66,82,129],"it":[30],"not":[32],"obvious":[33],"from":[34],"observed":[36,56],"importance":[39],"different":[40,44,87],"users":[41],"place":[42],"on":[43],"aspects":[45,88],"We":[49,74,95],"propose":[50],"a":[51,91,101,111,141],"probabilistic":[52],"modeling":[53],"aspect":[57,64],"to":[59,137],"infer":[60],"(i)":[61],"each":[62],"user's":[63],"bias":[65],"(ii)":[67],"latent":[68,92],"intrinsic":[69],"model":[75,128],"multi-aspect":[76],"as":[78],"ordered":[79],"discrete":[80],"data":[81],"encode":[83],"dependency":[85],"between":[86],"using":[90,100],"Gaussian":[93],"structure.":[94],"handle":[96],"Gaussian-Categorical":[98],"non-conjugacy":[99],"stick-breaking":[102],"formulation":[103],"coupled":[104],"with":[105],"P\\'{o}lya-Gamma":[106],"auxiliary":[107],"variable":[108],"augmentation":[109],"for":[110],"simple,":[112],"fully":[113],"Bayesian":[114],"inference.":[115],"On":[116],"two":[117],"real":[118],"world":[119],"datasets,":[120],"we":[121],"demonstrate":[122],"predictive":[124],"ability":[125],"our":[127],"its":[130],"effectiveness":[131],"learning":[133],"explainable":[134],"biases":[136],"provide":[138],"insights":[139],"towards":[140],"more":[142],"reliable":[143],"product":[144],"estimation.":[146]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
