• 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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