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المؤلفSardianos, Christos
المؤلفVarlamis, Iraklis
المؤلفChronis, Christos
المؤلفDimitrakopoulos, George
المؤلفAlsalemi, Abdullah
المؤلفHimeur, Yassine
المؤلفBensaali, Faycal
المؤلفAmira, Abbes
تاريخ الإتاحة2022-12-29T07:34:42Z
تاريخ النشر2021
اسم المنشورInternational Journal of Intelligent Systems
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1002/int.22314
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37802
الملخصThe 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
راعي المشروعThis 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.
اللغةen
الناشرJohn Wiley and Sons Ltd
الموضوعenergy efficiency
explainable recommendation system
Internet of things
recommendation systems
rule-based recommendation
user habits
العنوانThe emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency
النوعArticle
الصفحات656-680
رقم العدد2
رقم المجلد36
dc.accessType Abstract Only


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