This study investigates fairness in knowledge-aware recommender systems by evaluating their performance across both accuracy and fairness metrics. Using the MovieLens 1M dataset, we compare general, knowledge-aware, and fairness-optimized models through a custom RecBole-based pipeline. Results indicate knowledge-aware models offer some fairness benefits without major accuracy loss, though no model excels universally. Adjusting loss component weights reveals complex trade-offs and component importance, underscoring the need for nuanced fairness optimization.

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.

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.

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.

Recommender Systems are key instruments that are constantly being employed in the online environment as a method of connecting users and items. Due to the resulting personalised suggestions, users benefit from quickened decision making, however, such systems can also introduce unwanted side-effects, by reinforcing existing biases or limiting exposure to diverse content. While much of the research surrounding unfairness and its effects has been conducted specifically to benefit the users, the creators of the distributed items, namely the providers, can also be subject to inequity. From the perspective of a provider, garnering visibility and exposure of their items through these systems converts into revenue. Recommender Systems can be a positive means in sectors where, historically, groups of providers have been disadvantaged from reaching their desired consumers, although if mishandled can become an additional hindrance. Amongst the many relevant domains, the book industry is a clear example where authors have been discriminated based on traits unrelated to the quality of their writing. Publishers have manifested an adversity towards translated works leading to an innate disadvantage when it comes to their distribution and marketing. Still, the fairness of how recommender systems handle translated works when competing with other books remains unexplored. To address this gap, we conduct an empirical exploration of several state of the art recommendation algorithms, evaluating their performance with respects to accuracy and provider fairness. This allows us to discern if any of the algorithms are a helpful conduit or a harmful one. Upon identifying inequity concerning foreign authors, we probe the ability of mitigating it on an algorithmic level, by applying a reranking method. Outcomes stemming from this work, reveal high representation of books available in English in recommendations, while highlighting the advantages of foreign manuscripts with translations over those without. The outlined findings inform future design of Recommender Systems and their provider fairness evaluation.