In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Although recommender systems (RSs) play a crucial role in our society, previous studies
have revealed that the performance of RSs may considerably differ between groups of …

Information access research (and development) sometimes makes use of gender, whether
to report on the demographics of participants in a user study, as inputs to personalized …

Collaborative filtering algorithms find useful patterns in rating and consumption data and
exploit these patterns to guide users to good items. Many of the patterns in rating datasets …

As recommender systems play an important role in everyday life, there is an increasing
pressure that such systems are fair. Besides serving diverse groups of users, recommenders …

The majority of music consumption nowadays takes place on music streaming platforms.
Whichever artists, albums, or songs are exposed to consumers on these platforms therefore …

In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …

The performance of recommender systems highly impacts both music streaming platform
users and the artists providing music. As fairness is a fundamental value of human life, there …

Recommender systems have become the dominant means of curating cultural content,
significantly influencing the nature of individual cultural experience. While the majority of …