Laurito,WalterHöllig,JacquelineLachowitzer,JonasThoma,SteffenBudde,MatthiasPhilipp,PatrickDemmler, DanielKrupka, DanielFederrath, Hannes2022-09-282022-09-282022978-3-88579-720-3https://dl.gi.de/handle/20.500.12116/39525Data visualization is a complex task that typically requires human expertise, acquired through a large number of professional working hours. The automatic generation of reasonable visualizations would be a good solution for inexperienced laypeople. However, existing approaches fall short since they are quite static and rely only on traditional supervised learning. This results in models which recommend a single visualization solely based on the dataset features. User preferences and goals are not taken into account. We propose a more flexible solution that is iteratively updated with the individual user's preferences and outputs a ranked list of visualizations for a given dataset.enVisualization RecommendationAutomated Visualization DesignMachine LearningHuman PreferencesReinforcement LearningEvolutionary AlgorithmReward Learning10.18420/inf2022_271617-5468