Recommender Systems: An Explainable AI Perspective
Alexandra Vultureanu‐Albişi, Costin Bădică
Abstract
In recent years, in the era of information overload development, the need for recommender systems that make personalized suggestion systems has become a very exciting field for researchers. To develop models that generate high-quality recommendations, the explainable recommendation has been introduced, proposing to develop intuitive and trustworthy explanations. The problem that the explainable recommendation wants to solve is to let people understand why certain elements rather than other are recommended by the system. This paper briefly overviews the short history of explainable AI and then it presents its role and applicability in the domain of recommender systems. Our work contributes to understanding the concept of explainable recommendation and what it should accomplish to increase its acceptability and to enable its accurate evaluation.