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

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....

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Detalles Bibliográficos
Autores principales:	Wischenbart, Martin, Firmenich, Sergio Damián, Rossi, Gustavo Héctor, Bosetti, Gabriela Alejandra, Kapsammer, Elisabeth
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2021
Materias:	Informática Web augmentation Visual programming Client-side personalization End-user programming End-user development Controllability of recommender systems Browser-side trans-coding
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/138770
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-138770
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Informática Web augmentation Visual programming Client-side personalization End-user programming End-user development Controllability of recommender systems Browser-side trans-coding
spellingShingle	Informática Web augmentation Visual programming Client-side personalization End-user programming End-user development Controllability of recommender systems Browser-side trans-coding Wischenbart, Martin Firmenich, Sergio Damián Rossi, Gustavo Héctor Bosetti, Gabriela Alejandra Kapsammer, Elisabeth Engaging end-user driven recommender systems : Personalization through web augmentation
topic_facet	Informática Web augmentation Visual programming Client-side personalization End-user programming End-user development Controllability of recommender systems Browser-side trans-coding
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.
format	Articulo Articulo
author	Wischenbart, Martin Firmenich, Sergio Damián Rossi, Gustavo Héctor Bosetti, Gabriela Alejandra Kapsammer, Elisabeth
author_facet	Wischenbart, Martin Firmenich, Sergio Damián Rossi, Gustavo Héctor Bosetti, Gabriela Alejandra Kapsammer, Elisabeth
author_sort	Wischenbart, Martin
title	Engaging end-user driven recommender systems : Personalization through 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
publishDate	2021
url	http://sedici.unlp.edu.ar/handle/10915/138770
work_keys_str_mv	AT wischenbartmartin engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation AT firmenichsergiodamian engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation AT rossigustavohector engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation AT bosettigabrielaalejandra engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation AT kapsammerelisabeth engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation
bdutipo_str	Repositorios
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Engaging end-user driven recommender systems : Personalization through web augmentation

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