• How Can Digital Nudges Guide Users Towards More Diverse News Consumption?

    Author: Laura Modre Supervisor: Irina Nalis-Neuner, Co-Supervisor: Julia Neidhardt Abstract Most web-based news outlets employ recommendation algorithms which collect and process data on users’ previous behaviors and preferences to curate highly personalized news environments. Increasingly, there is a call for these systems to also incorporate insights from behavioral science to construct recommender systems that extend…

    Read more

  • Content-Based Restaurant Recommendation Systems Using Textual and Visual Data

    Author: Dante Godolja Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. By analyzing the content of restaurants, these systems can generate recommendations. Current research explores ways to improve their effectiveness. In this thesis we explore…

    Read more

  • Data Transformation Tool to Explore News Recommenders

    Author: Manuel Feuerstein Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract This thesis focuses on the development of a data transformation tool to analyze and leverage diverse data sources from Falter Verlagsgesellschaft m.b.H (Falter). Falter provides content-based data from platforms like and, along with user-based data from Matomo Analytics and shop purchase statistics.…

    Read more