Category: Thesis

  • The Impact of Using Large Language Models on the Performance of Recommender Systems

    Author: Michael Schmiedmayer Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne Abstract This thesis investigates the integration of Large Language Models (LLMs) into Conversational Recommender Systems (CRSs), evaluating the impact of model scale and hyperparameter configurations on retrieval performance. Moving beyond purely synthetic benchmarks, this research introduces a novel, end-to-end evaluation framework grounded in real-world user behaviour.…

  • Bias in Medical Recommendations: Prompting vs. Fine-tuning of Large Language Models

    Author: Daha Pavlovic Supervisor: Julia Neidhardt, Co-Supervisor: Bernhard Krüpl-Sypien Abstract A significant use case for large language models (LLMs) is the provision of medical advice, including diagnostic suggestions, treatment plans and healthcare recommendations. As individuals increasingly rely on LLMs for initial medical consultations either before visiting a healthcare professional or, in some cases, instead of…

  • Data-Centric AI for Conceptual Modeling: Cleansed Data and GNN-LLM based Recommender for UML Class Diagrams

    Author: Andjela Djelic Supervisor: Dominik Bork, Co-Supervisor: Julia Neidhardt Abstract In the Model-Driven Engineering (MDE) domain, Machine Learning (ML)-based recommender systems can assist developers during conceptual modeling by suggesting plausible modeling elements based on the current state of a model. However, the conceptual model datasets commonly used for such machine learning research, often mined from…

  • 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…

  • 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…

  • 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…