News – CDL-RecSys

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…