Author: Gwendolyn Rippberger
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Analysing Dynamics Over Time of Bias in Recommender Systems
Author: Boris Staykov Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Recommender systems play a pivotal role in personalizing user experiences across various domains such as e-commerce, news, and entertainment platforms. However, the presence of bias within these systems poses significant challenges, potentially leading to unfair treatment of users. This thesis addresses the critical issue…
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Evaluating the Fairness of News Recommender Algorithms Within Detected User Communities
Author: Bernhard Steindl Supervisor: Julia Neidhardt, Co-Supervisor: Mete Sertkan Abstract Recommender systems have become an essential part of our modern lives. Recommendation algorithms support users by recommending items tailored to their interests and by assisting in discovering new items. For example, some online news platforms use recommendation models to suggest news articles to users based…
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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…