Constructive Recommendation

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 systems can help users make good decisions over complex configuration spaces. Our goal is to adapt existing machine learning techniques, or devise new ones, in order to build recommendation systems suitable for constructive tasks.