Show simple item record

AuthorSardianos, Christos
AuthorVarlamis, Iraklis
AuthorChronis, Christos
AuthorDimitrakopoulos, George
AuthorAlsalemi, Abdullah
AuthorHimeur, Yassine
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:42Z
Publication Date2021
Publication NameInternational Journal of Intelligent Systems
ResourceScopus
URIhttp://dx.doi.org/10.1002/int.22314
URIhttp://hdl.handle.net/10576/37802
AbstractThe recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior. 2020 Wiley Periodicals LLC
SponsorThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10?0130?170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherJohn Wiley and Sons Ltd
Subjectenergy efficiency
explainable recommendation system
Internet of things
recommendation systems
rule-based recommendation
user habits
TitleThe emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency
TypeArticle
Pagination656-680
Issue Number2
Volume Number36
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record