News
Stay updated on our lab’s latest activities and projects through our news section featuring insightful blogs.
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Improving Trust in Recommender Systems through Context Clues
Author: Tobias Sippl Supervisor: Irina Nalis-Neuner, Co-Supervisor: Julia Neidhardt Abstract The COVID-19 pandemic and the resulting government measures have triggered a wave of protests and demonstrations in Austria. We saw some protestors resorting to populist rhetoric to express their dissatisfaction, which in some cases, led to anti-democratic tendencies. Populist talking points have not been confined…
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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…