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Creating and optimizing content- and dialogue-based recommendation processes and their GUIs (graphical user interfaces) manually is expensive and slow. Changes in the environment may also be found too late or even be overlooked by humans. We show how to generate such processes and their GUIs semi-automatically by using knowledge derived from unstructured data such as customer feedback on products on the Web. Our approach covers the whole lifecycle from knowledge discovery through text mining techniques to the use of this knowledge for semi-automatic generation of recommendation processes and their user interfaces as well as their comparison in real-world use within the e-commerce domain through A/B-variant tests. These tests indicate that our approach can lead to better results as well as less manual effort.