CP-nets are a very expressive graphical model for the representation of preferences over combinatorial spaces. They are particularly well suited for settings where an important task is to compute the optimal completion of some partially specified alternative; this is for instance the case of interactive configurators, where preferences can be used, at every step of the interaction, to guide the decision maker towards a satisfactory configuration. Learning CP-nets turns out to be challenging when the input data has the form of pairwise comparisons between alternatives. Furthermore, this type of preference data is not commonly stored: it can be elicitated but this puts an additional burden on the decision maker. In this article, we propose a new method for learning CP-nets from sales history, a kind of data readily available in many e-commerce applications. The approach is based on the the minimum description length (MDL) principle. We show some theoretical properties of this learning task, namely its sample complexity and its NP-completeness, and we experiment this learning algorithm in a recommendation settings with a real sales history from a car maker.