Abstract

Transparency and explainability are topics studied for more than two decades in the area of recommender systems, due to its impact on the user experience of personalized systems. Interestingly, only in recent years these topics have reached importance within Artificial Intelligence (AI) as a whole, under the umbrella of the term XAI (eXplainable AI). Some authors have shown that advances in XAI from different fields (computer science, design, HCI, IR, AI, etc.) have not been properly integrated into a common body of knowledge due to lack of connection among these communities. This talk gives one small step to bridge this gap, by showing how works on explainability, transparency, visualization, user interfaces and user control in recommender systems are significantly related to XAI and can inspire new ideas of research on visual XAI.