In this section
@inproceedings{Rago:2020:kr.2020/83,
author = {Rago, A and Cocarascu, O and Bechlivanidis, C and Toni, F},
doi = {kr.2020/83},
pages = {805--815},
publisher = {IJCAI},
title = {Argumentation as a framework for interactive explanations for recommendations},
url = {http://dx.doi.org/10.24963/kr.2020/83},
year = {2020}
}
TY - CPAPER
AB - As AI systems become ever more intertwined in our personallives, the way in which they explain themselves to and inter-act with humans is an increasingly critical research area. Theexplanation of recommendations is, thus a pivotal function-ality in a user’s experience of a recommender system (RS),providing the possibility of enhancing many of its desirablefeatures in addition to itseffectiveness(accuracy wrt users’preferences). For an RS that we prove empirically is effective,we show how argumentative abstractions underpinning rec-ommendations can provide the structural scaffolding for (dif-ferent types of) interactive explanations (IEs), i.e. explana-tions empowering interactions with users. We prove formallythat these IEs empower feedback mechanisms that guaranteethat recommendations will improve with time, hence render-ing the RSscrutable. Finally, we prove experimentally thatthe various forms of IE (tabular, textual and conversational)inducetrustin the recommendations and provide a high de-gree oftransparencyin the RS’s functionality.
AU - Rago,A
AU - Cocarascu,O
AU - Bechlivanidis,C
AU - Toni,F
DO - kr.2020/83
EP - 815
PB - IJCAI
PY - 2020///
SN - 2334-1033
SP - 805
TI - Argumentation as a framework for interactive explanations for recommendations
UR - http://dx.doi.org/10.24963/kr.2020/83
UR - http://hdl.handle.net/10044/1/80848
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:
To reach the network manager, Diana O'Malley - including to join the network - please contact: