Recommender Systems (RS) play a crucial role in shaping children's digital experience, by selecting which media they interact with. Through this selection process, RS not only influence immediate choices but may also contribute to the formation of preferences and worldviews. As is the case for all technologies, RS embody implicit and explicit design choices that reflect particular values. In this paper, we investigate the extent to which RS reflect children's values in the domain of books. Since books represent a popular medium of introducing moral concepts to children, we will focus on this domain, but our approach can be applied to other textual content as well. Given that children are in a formative developmental stage, their values are both distinct from those of adults and subject to change, making them especially susceptible to being influenced by RS. We first analyze children's book reviews to capture how values are expressed in practice. Based on these value expressions, we construct value profiles and obtain user segments through clustering, representing groups of children with similar value priorities and expressions. We use this in our analysis to gain insight into emerging patterns. Multiple recommendation strategies are employed to examine how well recommended items align with users' value profiles. By comparing the values reflected in recommended books with those expressed by users, we assess whether RS amplify, suppress, or remain neutral with respect to children's values. Our findings provide insight into how children manifest value preferences online and into the value alignment of RS for young users. ... Read more

Music-streaming platforms rely on recommender systems to help listeners navigate millions of tracks, including a growing number of children using these platforms. However, most systems are optimized for adults, often resulting in recommendations that fail to reflect preferences or needs of children. While demographic features have been shown to improve performance in models focused on adults, their impact on a child-centric recommender system remains unexplored. This study investigates whether incorporating demographic features (age, gender, and country) and profiling features (exploratoryness, concentration, and replayness) improves the quality of music recommendations for children. Using a filtered subset of the LFM-2b dataset, we evaluate a baseline model based on implicit-feedback interactions against variants extended with different combinations of demographic and profiling features. Results show that demographic features led to a reduced accuracy across most models. In contrast, profiling features significantly increase top-K accuracy, with improvements up to 18%. These findings highlight the limitations of recommender systems tuned for adults when applied to children and emphasize the value of behavioral-aware modeling in the development of more effective child-centric music recommender systems. ... Read more

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. ... Read more

The music listening preferences of children have been the subject of numerous studies, intending to inform the design of music recommender systems to better cater to children’s specific needs. Most of these studies are centered around genre, while few explore other traits of songs to capture listening behavior. In this research, we propose song structure, the arrangement of songs into sections, as a novel lens to analyze children’s music preferences. We create an extension from the popular LFM2b dataset to the Genius Lyrics dataset. Using this dataset, we group song interactions of users according to the educational level of children aged 12 through 17 and adults, and analyze and compare how song sections play a role in their listening behavior, based on the share of similarly structured songs in their listening data. To identify similar song structures, we cluster songs on their song structure fingerprints, extracted from their lyrics. We find no salient differences in the preferences of the children’s age groups, but we do find preliminary indications that adults interact less with songs characterized by the presence of lyrical Hooks (the most catchy parts of songs) than children and more with songs that are focused around Choruses. Although these insights can be used directly by recommender systems, our findings can be used as a springboard for future audio-based research to focus on not only lyrical, but also instrumental Hooks and their relation to children’s music preferences. ... Read more

As online video platforms like YouTube and YouTube Kids continue to shape young children's daily media use, concerns about their exposure to inappropriate content persist. While these platforms implement various safeguards to protect young audiences, inappropriate videos continue to surface in search results and next-video recommendations, sometimes even at the top of the list. This study explores how metadata-derived features can be used to identify and address such content. Drawing on a ground-truth dataset of YouTube videos labeled for toddler appropriateness, we conduct a detailed feature analysis to uncover patterns linked to (in)appropriateness and train a classifier capable of predicting video appropriateness for young children. Building on these insights, we develop and evaluate score-based reranking strategies designed to reduce exposure to inappropriate videos while promoting age-appropriate content. Our findings show that metadata-informed reranking significantly improves the prioritization of suitable content, raising HitRate@1 of suitable videos from 14% to as high as 62%, but also reveal critical trade-offs: misclassified inappropriate videos, particularly when predicted with high confidence, may still appear in top-ranked positions. As such, detection and reranking methods like ours represent a first step that warrants further steps for safer recommendation environments for young children. This research provides a practical framework for improving recommendation outcomes and contributes to the broader conversation on designing safer, more transparent, and child-centered media systems.

Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo ... Read more

Adolescence is a pivotal period of identity formation, significantly influenced by media consumption, particularly music. While recommender sytems (RS) play a central role in shaping adolescents' music exposure, they are typically designed without considering developmental needs. We do not yet understand how identity development manifests in behavioral data over time. And without this understanding, we cannot design systems that meaningfully support it. This study addresses this critical gap by applying James Marcia's established Identity Status Theory to real-world listening behavior to get insight into how adolescents traverse and transition betweeen distinct developmental phases.
Utilizing the LFM2-b dataset, we operationalize James Marcia's dimensions of exploration and commitment through Shannon Entropy and Jensen-Shannon Divergence of genre distributions, respectively, to identify and characterize distinct behavioral phases (Diffusion, Foreclosure, Moratorium, Achievement).
Our results show that while most users briefly enter all four phases, sustained engagement is much rarer, particularly for Foreclosure and Moratorium, which appear less stable. In contrast, Diffusion and Achievement are both more frequently sustained, with Achievement emerging as the most common final phase (40.78%). A strong directional transition was observed from Moratorium to Achievement (0.67 probability), while reciprocal transitions between Diffusion and Foreclosure highlight re-evaluation among users. These patterns support the dynamic and recursive nature of adolescent identity development. By linking digital behavior to psychological theory, this work offers both developmental insights and practical implications. It lays the foundation for designing recommender systems that are not only personalized but also support healthy identity formation.
... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more