Mouadh Guesmi
The literature on explainable recommendations is already rich. In this paper, we aim to shed light on an aspect that remains under- explored in this area of research, namely providing personalized explanations. To address this gap, we developed a transparent Rec- ommendation and Interest Modeling Application (RIMA) that pro- vides on-demand personalized explanations with varying levels of detail to meet the demands of different types of end-users. The results of a preliminary qualitative user study demonstrated po- tential benefits in terms of user satisfaction with the explainable recommender system. Our work would contribute to the litera- ture on explainable recommendation by exploring the potential of on-demand personalized explanations, and contribute to the prac- tice by offering suggestions for the design and appropriate use of personalized explanation interfaces in recommender systems. CCS CONCEPTS • Information systems →Personalization; Recommender sys- tems.