Advancing Media Recommendations through a User-Centric Understanding of Preferences, Experiences, and Agency

Media recommender systems increasingly shape how users discover, select, and experience audiovisual content. However, user preferences are often inferred from observable behaviors, such as ratings, clicks, and viewing histories. These indicators may not fully capture the multidimensional, dynamic, and context-dependent nature of preference or users’ expectations regarding relevance, content quality, and autonomy. This can create tensions between what users value, what they consume, and what platforms recommend.

This project takes a user-centric approach to examining how preferences are expressed, negotiated, and conditionally enacted in users’ interactions with media recommender systems. It investigates how preferences are conceptualized and operationalized, how users experience algorithmic recommendations, and how they navigate and respond to recommendations in everyday media use. Ultimately, the project seeks to inform more responsive and beneficial approaches to personalization that better account for users’ diverse and contextual preferences, experiences, and agency.