Music recommender systems are a widely adopted application of personalized systems and interfaces. By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods), or a mixture of these with other features (hybrid methods). In this chapter, we focus on the listener’s aspects of music recommender systems. We discuss different factors influencing relevance for recommendation on both the listener’s and the music’s side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i. e., personality traits and demographic characteristics, and cultural features, i. e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior, and (iii) listener intention, in particular by studying music information behavior, i. e., how people seek, find, and use music information. This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.