In this section
@article{Rago:2021:10.1016/j.artint.2021.103506,
author = {Rago, A and Cocarascu, O and Bechlivanidis, C and Lagnado, D and Toni, F},
doi = {10.1016/j.artint.2021.103506},
journal = {Artificial Intelligence},
pages = {1--22},
title = {Argumentative explanations for interactive recommendations},
url = {http://dx.doi.org/10.1016/j.artint.2021.103506},
volume = {296},
year = {2021}
}
TY - JOUR
AB - A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the systematic definition of explanations for outputs in such a way that both the explanations and the systems themselves are able to adapt to their human users' needs. In this paper we propose an RS hosting a vast repertoire of explanations, which are customisable to users in their content and format, and thus able to adapt to users' explanatory requirements, while being reasonably effective (proven empirically). Our RS is built on a graphical chassis, allowing the extraction of argumentation scaffolding, from which diverse and varied argumentative explanations for recommendations can be obtained. These recommendations are interactive because they can be questioned by users and they support adaptive feedback mechanisms designed to allow the RS to self-improve (proven theoretically). Finally, we undertake user studies in which we vary the characteristics of the argumentative explanations, showing users' general preferences for more information, but also that their tastes are diverse, thus highlighting the need for our adaptable RS.
AU - Rago,A
AU - Cocarascu,O
AU - Bechlivanidis,C
AU - Lagnado,D
AU - Toni,F
DO - 10.1016/j.artint.2021.103506
EP - 22
PY - 2021///
SN - 0004-3702
SP - 1
TI - Argumentative explanations for interactive recommendations
T2 - Artificial Intelligence
UR - http://dx.doi.org/10.1016/j.artint.2021.103506
UR - https://www.sciencedirect.com/science/article/pii/S0004370221000576?via%3Dihub
UR - http://hdl.handle.net/10044/1/89312
VL - 296
ER -
