We present an EM-algorithm for the problem of learning preferences with Gaussian processes in the context of multi-task learning. We validate our approach on an audiological data set and show that predictive results for sound quality perception of normal-hearing and hearing-impaired subjects, in the context of pairwise comparison experiments, can be improved using a hierarchical model. Key words: preference learning, multi-task learning, hierarchical modeling, Gaussian processes