Category: Master Thesis

  • Deep Learning-Based Stock Price Prediction

    Author: Fung Yee Tang Supervisor: Julia Neidhardt Abstract For many years, stock price prediction has been a challenging task due to market volatility and the complex, nonlinear factors that influence financial markets. While traditional methods such as fundamental and technical analysis are widely used, they often fail to capture the intricate patterns in modern financial…

  • Exploration of Intermediate Fusion Strategies: Between Graph and Text Modalities in Session-Based Recommender Systems

    Author: Roman Grebnev Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne Abstract Online platforms often recommend items to users based on their current browsing activity, especially when long-term user history isn’t available. These Session-Based Recommender Systems typically rely on the sequence of items a user interacts with. However, just looking at the sequence (like a graph of…

  • 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…