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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/138404
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/138404 |
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CONICET Digital (CONICET) |
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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 |
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1846083356622585856 |
score |
13.22299 |