Abstract Constructive recommendation is the task of recommending object “configurations”, i.e. objects that can be assembled from their components on the basis of the user preferences. Examples include: PC configurations, recipes, travel plans, layouts, and other structured objects. Recommended objects are created by maximizing a learned utility function over an exponentially (or even infinitely) large combinatorial space of configurations. The utility function is learned through preference elicitation, an interactive process for collecting user feedback about recommended objects. Constructive recommendation brings up a wide range of possible applications as well as many untackled research problems, ranging from the unprecedented complexity of the inference problem to the nontrivial choice of the type of user interaction