Conversational Recommender Systems assist online users in their information-seeking and decision making tasks by supporting an interactive process with the aim of finding the most appealing items according to the user preferences. Unfortunately, collecting dialogues data to train these systems can be labour-intensive, especially for data-hungry Deep Learning models. Therefore, we propose an automatic procedure able to generate plausible dialogues from recommender systems datasets.