| Summary: | This research work describes an architecture for building a system that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in a manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides the means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. We compare such strategies through a set of experiments to understand how they balance learning and provide accurate media news recommendations to the user. The media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.
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