Engaging end-user driven recommender systems: personalization through web augmentation. Wischenbart, M., Firmenich, S., Rossi, G., Bosetti, G., & Kapsammer, E. Multimed. Tools Appl., 80(5):6785–6809, February, 2021. ![Engaging end-user driven recommender systems: personalization through web augmentation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{wischenbart_engaging_2021,
title = {Engaging end-user driven recommender systems: personalization through web augmentation},
volume = {80},
issn = {1380-7501},
url = {https://doi.org/10.1007/s11042-020-09803-8},
doi = {10.1007/s11042-020-09803-8},
abstract = {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.},
number = {5},
journal = {Multimed. Tools Appl.},
author = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Bosetti, Gabriela and Kapsammer, Elisabeth},
month = feb,
year = {2021},
pages = {6785--6809},
}
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