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.

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.

Recommender systems play a considerable role in the consumption of music, also for children. Children are easily influenced, inappropriate song lyrics can negatively impact children's behaviour and personality, by teaching inappropriate language or harmful biases. We argue for a need for recommenders to protect children from inappropriate content. Recommender systems should consider what content is and is not well-suited for children. To guide future design and development of recommender systems for children, this research probes the music lyrics of Pop, Rock, Country, Rap, and R&B for inappropriate content. We achieve this by an empirical analysis using four existing algorithms to scan different facets of inappropriate content in the lyrics in a dataset of 37,993 songs from the Genius database. The outcome of this empirical exploration reveals that Rap and R&B are the most inappropriate music genres and advises to be cautious when recommending these genres to children. Results also show that inappropriate content is highly prevalent in music. We address the need for a filter for recommender systems, to filter inappropriate songs for children.

Music recommender systems are increasingly present in our lives, and it is important to keep trying to improve recommendations in order to make them match the users preferences as well as possible. To achieve this, a vast amount of song and user data has to be analysed and taken into account. One of the approaches to do this, includes analyzing different audio features in order to find other songs with similar traits. The majority of the research and data in this sector is focused around adults, with little research surrounding children, which can result in worse recommendations for this demographic. In this paper, the focus is shifted towards children with the purpose of filling that gap. This is achieved by examining the prominence of specific song features among children of different age groups, expanding the knowledge on listening habits of a major demographic. More specifically, the research presented in this paper explores the prominence of various song features, aiming to find a connections between these features and the listening habits of children of specific age ranges from 8-18. This paper’s conclusions will offer potential enhancements, which can improve existing recommender systems by considering findings for their design. These findings will therefore allow for a more tailored experience for children of different age ranges, increasing overall user experience.

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.