Towards a Recommender Engine for Personalized Visualizations

Autores
Mutlu, Belgin; Veas, Eduardo Enrique; Trattner, Christoph; Sabol, Vedran
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating them requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad to account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that can suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
Fil: Mutlu, Belgin. Know-Center GmbH; Austria
Fil: Veas, Eduardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Know-Center GmbH; Austria
Fil: Trattner, Christoph. Know-Center GmbH; Austria
Fil: Sabol, Vedran. Know-Center GmbH; Austria
Materia
Collaborative Filtering
Crowd-Sourcing
Personalized Visualizations
Recommender Systems
Visualization Recommender
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/59455
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/59455
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Towards a Recommender Engine for Personalized VisualizationsMutlu, BelginVeas, Eduardo EnriqueTrattner, ChristophSabol, VedranCollaborative FilteringCrowd-SourcingPersonalized VisualizationsRecommender SystemsVisualization Recommenderhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating them requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad to account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that can suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.Fil: Mutlu, Belgin. Know-Center GmbH; AustriaFil: Veas, Eduardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Know-Center GmbH; AustriaFil: Trattner, Christoph. Know-Center GmbH; AustriaFil: Sabol, Vedran. Know-Center GmbH; AustriaSpringer2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59455Mutlu, Belgin; Veas, Eduardo Enrique; Trattner, Christoph; Sabol, Vedran; Towards a Recommender Engine for Personalized Visualizations; Springer; Lecture Notes in Computer Science; 9146; 6-2015; 169-1820302-9743CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-20267-9_14info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-319-20267-9_14info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:02Zoai:ri.conicet.gov.ar:11336/59455instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:03.115CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Towards a Recommender Engine for Personalized Visualizations
title Towards a Recommender Engine for Personalized Visualizations
spellingShingle Towards a Recommender Engine for Personalized Visualizations
Mutlu, Belgin
Collaborative Filtering
Crowd-Sourcing
Personalized Visualizations
Recommender Systems
Visualization Recommender
title_short Towards a Recommender Engine for Personalized Visualizations
title_full Towards a Recommender Engine for Personalized Visualizations
title_fullStr Towards a Recommender Engine for Personalized Visualizations
title_full_unstemmed Towards a Recommender Engine for Personalized Visualizations
title_sort Towards a Recommender Engine for Personalized Visualizations
dc.creator.none.fl_str_mv Mutlu, Belgin
Veas, Eduardo Enrique
Trattner, Christoph
Sabol, Vedran
author Mutlu, Belgin
author_facet Mutlu, Belgin
Veas, Eduardo Enrique
Trattner, Christoph
Sabol, Vedran
author_role author
author2 Veas, Eduardo Enrique
Trattner, Christoph
Sabol, Vedran
author2_role author
author
author
dc.subject.none.fl_str_mv Collaborative Filtering
Crowd-Sourcing
Personalized Visualizations
Recommender Systems
Visualization Recommender
topic Collaborative Filtering
Crowd-Sourcing
Personalized Visualizations
Recommender Systems
Visualization Recommender
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating them requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad to account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that can suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
Fil: Mutlu, Belgin. Know-Center GmbH; Austria
Fil: Veas, Eduardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Know-Center GmbH; Austria
Fil: Trattner, Christoph. Know-Center GmbH; Austria
Fil: Sabol, Vedran. Know-Center GmbH; Austria
description Visualizations have a distinctive advantage when dealing with the information overload problem: since they are grounded in basic visual cognition, many people understand them. However, creating them requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad to account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that can suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/59455
Mutlu, Belgin; Veas, Eduardo Enrique; Trattner, Christoph; Sabol, Vedran; Towards a Recommender Engine for Personalized Visualizations; Springer; Lecture Notes in Computer Science; 9146; 6-2015; 169-182
0302-9743
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59455
identifier_str_mv Mutlu, Belgin; Veas, Eduardo Enrique; Trattner, Christoph; Sabol, Vedran; Towards a Recommender Engine for Personalized Visualizations; Springer; Lecture Notes in Computer Science; 9146; 6-2015; 169-182
0302-9743
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-20267-9_14
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-319-20267-9_14
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268833289601024
score 13.13397

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