Learning the preferences of other people is crucial for predict- ing future behavior. Both children and adults make inferences about others’ preferences from sparse data and in situations where the preferences have complex internal structures. We present a computational model of learning structured prefer- ences which integrates Bayesian inference and utility-based models of preference from economics. We experimentally test this model with adult participants, and compare the model to alternative heuristic models.