The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize …

Recommender systems are widely used in today’s society, but many of them do not meet users’ needs and therefore fail to reach their full potential. Without careful consideration, such systems can interfere with the natural decision-making process, resulting in the disregard for recommendations provided. …

Nowadays, one of the major current health risks is excessive sitting during work hours. Furthermore, the coronavirus disease 2019 (COVID-19) pandemic and the corresponding government state of emergency forced many people to work from home. These constraints carried out an important change in the lifestyle …

Design education has a nuanced relationship with examples. Although they are considered useful teaching tools, their use is often restricted to illustrating the design theories and principles around which the curriculum is structured. In contrast, professional designers view examples as autonomous entities …

Social networks and news outlets use recommender systems to distribute information and suggest news to their users. These algorithms are an attractive solution to deal with the massive amount of content on the web [6]. However, some organisations prioritise retention and maximisation of the number of …

Social networks and news outlets entrust content curation to specialised algorithms from the broad family of recommender systems. Companies attempt to increase engagement by connecting users with ideas they are more likely to agree with. Eli Pariser, the author of the term filter bubble, suggested that …

Recommendation systems, recommenders in short, selecting and filtering content are widely used by companies in order to provide suggestions for items to users. These ‘items’ range from songs (e.g., Spotify), series (e.g., Netflix), and movies (e.g., YouTube) to messages (e.g., Facebook), job vacancies …

Personalization of media services is gaining more and more traction, e.g., through the rise of personalization driven by recommender systems across media outlets. At the same time, we see a general rise in distrust and skepticism around the collection and processing of personal data, spurred by policy …