News
Stay updated on our lab’s latest activities and projects through our news section featuring insightful blogs.
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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…
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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…
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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 falter.at and shop.falter.at, along with user-based data from Matomo Analytics and shop purchase statistics.…