Engaging end-user driven recommender systems: personalization through web augmentation

Autores
Wischenbart, Martin; Firmenich, Sergio Damian; Rossi, Gustavo Héctor; Bosetti, Gabriela Alejandra; Kapsammer, Elisabeth
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.
Fil: Wischenbart, Martin. Johannes Kepler University Linz; Austria
Fil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austria
Materia
BROWSER-SIDE TRANS-CODING
CLIENT-SIDE PERSONALIZATION
CONTROLLABILITY OF RECOMMENDER SYSTEMS
END-USER DEVELOPMENT
END-USER PROGRAMMING
VISUAL PROGRAMMING
WEB AUGMENTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/138404
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/138404
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Engaging end-user driven recommender systems: personalization through web augmentationWischenbart, MartinFirmenich, Sergio DamianRossi, Gustavo HéctorBosetti, Gabriela AlejandraKapsammer, ElisabethBROWSER-SIDE TRANS-CODINGCLIENT-SIDE PERSONALIZATIONCONTROLLABILITY OF RECOMMENDER SYSTEMSEND-USER DEVELOPMENTEND-USER PROGRAMMINGVISUAL PROGRAMMINGWEB AUGMENTATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.Fil: Wischenbart, Martin. Johannes Kepler University Linz; AustriaFil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Kapsammer, Elisabeth. Johannes Kepler University Linz; AustriaSpringer2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138404Wischenbart, Martin; Firmenich, Sergio Damian; Rossi, Gustavo Héctor; Bosetti, Gabriela Alejandra; Kapsammer, Elisabeth; Engaging end-user driven recommender systems: personalization through web augmentation; Springer; Multimedia Tools And Applications; 80; 5; 10-2021; 6785-68091380-7501CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11042-020-09803-8info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11042-020-09803-8info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:20:48Zoai:ri.conicet.gov.ar:11336/138404instacron: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:34982025-10-15 15:20:48.318CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Engaging end-user driven recommender systems: personalization through web augmentation
title Engaging end-user driven recommender systems: personalization through web augmentation
spellingShingle Engaging end-user driven recommender systems: personalization through web augmentation
Wischenbart, Martin
BROWSER-SIDE TRANS-CODING
CLIENT-SIDE PERSONALIZATION
CONTROLLABILITY OF RECOMMENDER SYSTEMS
END-USER DEVELOPMENT
END-USER PROGRAMMING
VISUAL PROGRAMMING
WEB AUGMENTATION
title_short Engaging end-user driven recommender systems: personalization through web augmentation
title_full Engaging end-user driven recommender systems: personalization through web augmentation
title_fullStr Engaging end-user driven recommender systems: personalization through web augmentation
title_full_unstemmed Engaging end-user driven recommender systems: personalization through web augmentation
title_sort Engaging end-user driven recommender systems: personalization through web augmentation
dc.creator.none.fl_str_mv Wischenbart, Martin
Firmenich, Sergio Damian
Rossi, Gustavo Héctor
Bosetti, Gabriela Alejandra
Kapsammer, Elisabeth
author Wischenbart, Martin
author_facet Wischenbart, Martin
Firmenich, Sergio Damian
Rossi, Gustavo Héctor
Bosetti, Gabriela Alejandra
Kapsammer, Elisabeth
author_role author
author2 Firmenich, Sergio Damian
Rossi, Gustavo Héctor
Bosetti, Gabriela Alejandra
Kapsammer, Elisabeth
author2_role author
author
author
author
dc.subject.none.fl_str_mv BROWSER-SIDE TRANS-CODING
CLIENT-SIDE PERSONALIZATION
CONTROLLABILITY OF RECOMMENDER SYSTEMS
END-USER DEVELOPMENT
END-USER PROGRAMMING
VISUAL PROGRAMMING
WEB AUGMENTATION
topic BROWSER-SIDE TRANS-CODING
CLIENT-SIDE PERSONALIZATION
CONTROLLABILITY OF RECOMMENDER SYSTEMS
END-USER DEVELOPMENT
END-USER PROGRAMMING
VISUAL PROGRAMMING
WEB AUGMENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.
Fil: Wischenbart, Martin. Johannes Kepler University Linz; Austria
Fil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austria
description In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
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/11336/138404
Wischenbart, Martin; Firmenich, Sergio Damian; Rossi, Gustavo Héctor; Bosetti, Gabriela Alejandra; Kapsammer, Elisabeth; Engaging end-user driven recommender systems: personalization through web augmentation; Springer; Multimedia Tools And Applications; 80; 5; 10-2021; 6785-6809
1380-7501
CONICET Digital
CONICET
url http://hdl.handle.net/11336/138404
identifier_str_mv Wischenbart, Martin; Firmenich, Sergio Damian; Rossi, Gustavo Héctor; Bosetti, Gabriela Alejandra; Kapsammer, Elisabeth; Engaging end-user driven recommender systems: personalization through web augmentation; Springer; Multimedia Tools And Applications; 80; 5; 10-2021; 6785-6809
1380-7501
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/s11042-020-09803-8
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11042-020-09803-8
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/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_ 1846083356622585856
score 13.22299

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