Tag: Fairness
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User Profiling with Graph Neural Networks: Assessing Fairness and Explainability
Supervisor: Julia Neidhardt
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Evaluating the Fairness of News Recommender Algorithms Within Detected User Communities
Author: Bernhard Steindl Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Awards: Bernhard Steindl won the ASAI Masterthesis Prize 2025 (Informatics Website) Abstract This work addresses the problem of unfair treatment of different user groups in the recommendations they receive from recommendation algorithms. Recommender systems (RS) are algorithms that suggest items to a user that are…
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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,…