Children are generally underrepresented in music recommender system research, despite having distinct preferences and developmental needs that set them apart from adult audiences. Traditional recommender approaches, designed primarily for adults, often fail to capture the unique listening behaviors of younger users and may fail to serve them effectively. At the same time, acoustic features play a significant role in shaping children's music preferences, yet their potential to enhance and provide optimal recommendations for children remains largely unexplored.

To address this gap, our study examines whether extending a standard collaborative filtering recommender with individual acoustic features can yield more age-appropriate music suggestions for children. We integrate content-based attributes, such as acousticness, danceability, energy, instrumentalness, liveness, loudness, mode, speechiness, tempo, and valence, into an item-based collaborative filtering algorithm and evaluate its performance on users aged 15 through 18. By comparing accuracy and
diversity metrics before and after the inclusion of each feature, we identify which acoustic feature improves recommendation quality for each age group and results in the highest performance.

Our findings emphasize the significance of acoustic features, including mode, instrumentalness, and acousticness, in improving performance metrics for distinct age groups. By identifying these age-specific features, our research contributes to the development of age-appropriate and child-centric music recommender systems.