Discovering the Unknown Suggestion: a Short Review on Explainability for Recommender Systems

Artificial Intelligence and in particular machine learning and deep learning models are normally considered to be fast and high performing, but in general there is a lack of transparency and interpretability. The issues related to explainability and its consequences are becoming more and more relevant in the whole broad scenario of Artificial Intelligence. To address this issue, explainable AI emerged, as a set of Artificial Intelligence techniques able to make their own decision more transparent and interpretable, so as to let users understand the specific reasons why the system provided its outcome, decision, or, in the case of recommender systems, its suggestions. Explainable Artificial Intelligence is deeply needed in heterogeneous domains and contexts, as the need for transparency, interpretability and even accountability of the Artificial Intelligence-based systems is a big necessity, as confirmed by the recent right to explanation in the 2018 General Data Protection Regulation by the European Union. Due to the diffusion of recommender systems in many applicative domains and situations in everyday life and business fields, there is an emerging necessity for systems not only able to provide human decision-makers with suggestions and ease the decision-making processes in organizations, but also to give the right motivations of their recommendations. This paper summarizes the results of the study of the state of the art for Explainable Artificial Intelligence for Recommender Systems. We will follow the main reviews in literature to present the main work, kinds of explanainable recommendations and methods.