Children spend a significant amount of time listening to music. Music has a significant cognitive and developmental effect on them. Because of their unique behavioral characteristics and their emotion regulation skills, their music preferences differ significantly from adults. However, limited study has been conducted on their music preferences and no music recommendation system have been designed to cater for their specific needs and preferences. With this study, we conduct an empirical exploration of the music preferences of children in terms of audio and sentiment characteristics, readability and topics discussed in the songs listened by children at different ages. We utilize the outcomes of our empirical exploration to adapt a recommender system to cater for the music preferences of children. Both the empirical exploration and the evaluation of the recommender system is based on the well-known extensive music dataset LastFM-2b. Outcomes from this work showcase that grade school students prefer more positive and joyful sentiments expressed in the lyrics, simpler language and higher acousticness. Older children prefer songs that convey sadness or anger, higher language complexity and songs they can dance to. Incorporating the song features into a recommender system leads to an increase in terms of all evaluation metrics in children compared to the RS trained only on user-item interaction data.