The emergence of Explainability of Intelligent Systems: Delivering
  Explainable and Personalised Recommendations for Energy Efficiency
  The 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 in
order to increase user trust and improve the acceptance of recommendations. In
this work, 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.
