Category: Thesis

  • Choice-Based Preference Elicitation to Reduce the Cold Start Problem of a Leisure Activities Recommender in a Mobile App

    Author: Andreas Fink Supervisor: Julia Neidhardt Abstract Early stages of user interactions pose a significant challenge for recommender systems, particularly due to the cold start problem. This issue arises when new users or items lack historical data, making it difficult to generate personalized recommendations. This thesis investigates the effectiveness of four visual preference elicitation methods…

  • Comparative Analysis of Fashion Captioning and Multimodal Fashion Recommendation

    Author: Maria De Los Angeles Gwendolyn Aglae Rippberger Fonseca Supervisor: Julia Neidhardt Abstract This thesis explores two main tasks: (1) fine-tuning image captioning models for fashion datasets and (2) evaluating different feature spaces for personalized fashion recommendations. We fine-tune state-of-the-art vision-language models – BLIP-2 and LLaVA – on two fashion datasets, H&M and FACAD, to…

  • Context over Categories – Implementing the Theory of Constructed Emotion

    Author: Nils Klüwer Supervisor: Julia Neidhardt, Co-Supervisor: Irina Nalis-Neuner Abstract Emotion analysis is a critical research area with applications ranging from content moderation to personalized systems. Despite its importance, many approaches rely on traditional models, such as Ekman’s universal emotions theory, which reduces emotions to static, predefined categories. This oversimplification neglects the complexity and contextual…

  • 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…

  • Exploration of Content-Based Cross-Domain Podcast Recommender Systems

    Author: Matthias Hofmaier Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Podcasts have become a popular medium in the last decade. The huge amount of data available motivates research on podcast recommender systems to make this data accessible to users. Since interaction-based datasets for podcasts are only available to the large streaming providers, content-based methods…

  • Measuring Controversy in Online Discussions

    Author: Ivan Andreev Supervisor: Stefan Woltran, Co-Supervisor: Julia Neidhardt Abstract In today’s digital era, analyzing and improving online discourse is crucial. Polarisation is indeed a significant issue in contemporary society, particularly amplified within online discussions. The ability to accurately measure polarisation in these contexts is crucial for understanding societal dynamics and addressing associated challenges effectively.…