Personalization is the next step in order to improve the user experience of recommender systems. With personalization, systems are able to adapt in order to give users an experience that is tailored to their behavior, preferences, and needs. To achieve personalization, systems create user models based on user-generated data, so that a suitable adaptation strategy can be applied. The problem that persists is how to gather enough high-quality data about the (new) user to create accurate models. One way to solve this problem is to use questionnaires. However, this is not desirable since it is obtrusive, takes a lot of effort and time from the user, and thereby disrupts their interaction with the system. In this dissertation users' personality and their cultural background are considered for user modeling. These two constructs have shown to be enduring and stable, and encompass behavior, preferences, and needs in real-life situations. However, how these two constructs relate in a technological context is still relatively unknown. The results presented in this dissertation provide new insights in how users' personality and cultural background influences behavior, preferences, and needs in a music context. The presented works also show how personality and cultural dimensions can be obtained from SNSs data. The works together provide a novel way and a comprehensive view for creating a personalized music recommender system, especially in cases when users' behavioral data with the system is (still) limited.