Configuring complex products can be a challenge due to the huge number of configuration possibilities. In this paper, our goal is to foster the development of intelligent configuration assistants that can support customers in configuring

complex products. We formalize the task as a machine learning problem and in particular as a learning-to-rank problem. Given pairwise preferences elicited from experts, we show that we can train a model using support vector machines that ranks possible products according to their relevance to a given set of requirements specified by a user.