Recommender systems play a large role on contemporary music platforms, but they tend to work less well for non-mainstream listeners such as children. Additionally, there is no one strategy to perfectly capture a listener's music preference. As children develop understanding of music in different stages, using features they respond to might make recommendations more accurate. Therefore, this study seeks to investigate the effectiveness of a recommender which utilises matrix factorisation augmented with the musical features of tempo, mode, dynamics and time signature in recommending songs a child user would like. We evaluate the quality of this recommender based on the Factorisation Machine algorithm by comparing it to a non-augmented variant of the same algorithm and similar ones using fewer of the same features. Results show that while adding features improves the quality of recommendations, adding too many or the wrong features diminishes said improvement, although more research is needed in this direction.