The last few years saw a growing interest in Knowledge-Aware Recommender Systems (KARSs), given their capability in encoding and exploiting several data sources, both structured (such as \textit{knowledge graphs}) and unstructured (such as plain text); indeed, several pieces of research show the competitiveness of these models. Nowadays, a lot of models at the state-of-the-art in KARSs use deep learning, enabling them to exploit large amounts of information, including knowledge graphs (KGs), user reviews, plain text, and multimedia content (pictures, audio, videos). In my Ph.D. I will explore and study techniques for designing KARSs leveraging embeddings deriving from multi-modal information sources; the models I will design will aim at providing fair, accurate, and explainable recommendations.