Abstract. This publication presents our work on recommender systems for mobile audio-visual content. Our approach generates recommendations for media by extracting metadata and matching it with user-centric criteria such as mood preferences. We address the specific issues arising from mobility such as the need to minimize CPU-load, interaction complexity, as well as learning effort required from the user and the system