It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.