News
Stay updated on our lab’s latest activities and projects through our news section featuring insightful blogs.
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Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping
Author: Blake Huebner Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although fairness and diversity metrics are widely discussed,…
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COVID-19 and Populism in Austrian News User Comments – A Machine Learning Approach
Author: Ahmadou Wagne Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb 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…
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Unlocking the Potential of Content-Based Restaurant Recommender Systems: Insights from ENTER Conference 2024
Today, Thomas E. Kolb, had the pleasure of presenting our work, “Unlocking the Potential of Content-Based Restaurant Recommender Systems” at the ENTER Konferenze 2024, in Izmir, Turkey. 📚 Our research focuses on optimizing dining experiences by examining the effectiveness of TF-IDF and SBERT models. This study not only assesses these models on a technical level…