Tag: RecommenderSystems

  • Analysing Dynamics Over Time of Bias in Recommender Systems

    Author: Boris Staykov Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Recommender systems play a pivotal role in personalizing user experiences across various domains such as e-commerce, news, and entertainment platforms. However, the presence of bias within these systems poses significant challenges, potentially leading to unfair treatment of users. This thesis addresses the critical issue…

  • Advancing Session-Based Recommendations: Integrating Modern LLMs and Micro-Behaviors

    Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne

  • Survey Paper Just Accepted at ACM TORS

    We are pleased to share that our survey paper, “Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review,” has been accepted for publication in ACM Transactions on Recommender Systems (TORS). The paper provides a comprehensive review of research on Generative Conversational Recommender Systems (Gen-CRS). As large language models and other generative technologies increasingly influence…

  • 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.…

  • 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…

  • RecSys Summer School 2024

    📢Highlights from the 2024 RecSys Summer School in Bari, Italy! 🌍From October 8 to 12, the 2024 RecSys Summer School took place as a pre-program of this year’s ACM RecSys Conference. Co-funded by SIGCHI via its conference development fund, the school gathered leading experts to explore the latest developments in recommender systems. One of the…