Author: recsys-admin

  • 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…

  • 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…

  • 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…