The exponential growth of online content and consumer options has increased the reliance on recommender systems. Children, as a distinct user group, require tailored recommender systems different from those for adults. However, research on recommendation models for children is limited. This study evaluates deep learning recommendation models according to several performance and beyond-performance metrics on data from underage users on the Last.fm streaming platform, offering insights into optimal recommendation strategies for this demographic. Traditional non-deep learning models are used as baselines.