dc.creatorWischenbart, Martin
dc.creatorFirmenich, Sergio Damián
dc.creatorRossi, Gustavo Héctor
dc.creatorBosetti, Gabriela Alejandra
dc.creatorKapsammer, Elisabeth
dc.date2021
dc.date2022-07-04T12:31:44Z
dc.date.accessioned2023-07-15T04:42:07Z
dc.date.available2023-07-15T04:42:07Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/138770
dc.identifierissn:1380-7501
dc.identifierissn:1573-7721
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7470557
dc.descriptionIn 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.
dc.descriptionLaboratorio de Investigación y Formación en Informática Avanzada
dc.formatapplication/pdf
dc.format6785-6809
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0)
dc.subjectInformática
dc.subjectWeb augmentation
dc.subjectVisual programming
dc.subjectClient-side personalization
dc.subjectEnd-user programming
dc.subjectEnd-user development
dc.subjectControllability of recommender systems
dc.subjectBrowser-side trans-coding
dc.titleEngaging end-user driven recommender systems : Personalization through web augmentation
dc.typeArticulo
dc.typeArticulo


Este ítem pertenece a la siguiente institución