Tag: LLMs
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Advancing Session-Based Recommendations: Integrating Modern LLMs and Micro-Behaviors
Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne
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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…
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Examining Tech Bias: Analyzing Career Recommendations in LLMs through Demographic Persona-Based Prompting
Supervisor: Julia Neidhardt, Co-Supervisor: Bernhard Krüpl-Sypien
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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.…
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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…