Samenvatting

This report presents the design, implementation, and evaluation of a content-based recommender system for NEMO Kennislink, developed within the AI, Media and Democracy Lab (AIMD). The project investigates whether algorithmic recommendations can stimulate article discovery on a public science platform while respecting editorial and ethical principles. A content-based recommender was designed and tested in an online experiment using the Informfully research platform. Results show that generated recommendations broadened exposure patterns (i.e., increased catalogue coverage across article age, authors, and theme/series) while maintaining engagement levels comparable to editorial selections, demonstrating the potential of responsible and transparent recommender design for NEMO Kennislink.