Category: Bachelor Thesis
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Temporal Analysis of Session Clusters in Clickstream Data from a Price Comparison Platform
Author: Luca Turin Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne Abstract Understanding user behaviour is essential for designing digital services, improving user experience, and optimising commercial outcomes. This thesis investigates how behaviour varies over time and across contexts on a major price comparison platform. Building on prior work that offered a static segmentation of sessions, it…
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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.…
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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…
