Music plays a crucial role in children’s development by helping them express their identity, teaching them to belong to a culture, and developing their cognitive well-being and inner self-worth. Most music nowadays is consumed through online streaming websites like Spotify, which make use of recommendation systems to suggest new tracks. These recommendation algorithms internally make use of user and song features that aid in making predictions. However, as most of the research literature focuses on making recommendations for the adult group, few studies explore what makes the recommended songs appealing to children. In this paper, we perform user modelling techniques on the listening history of the children combined with high-level descriptors of the songs in order to capture their music preferences. By constructing user profiles, we aim to identify the characteristics of music that resonate most with the listener. In our research, we focus on children belonging to the age group of 6-17 years. The goal is to aid
in the design of future recommender systems that operate with greater transparency, allowing the impact of the user choices to be clearly observed by the consumer.