We study a context-aware preference learning system that automatically learns user preferences in different environments. The system is based on a Dirichlet process Gaussian mixture model and comprises an environmental model and a preference learning system. The environmental model is used to determine the user’s current environment, based on environmental signals. The preference learning system actively learns user preferences through pairwise comparison trials. During these trials, the so-called reference and alternative proposals are generated by the system and presented to a user, who, in his/her turn, indicates the preferred one. We propose a novel inference approach that allows for environmental model per-sonalization and leads to improved system performance. This is achieved by introducing a two-way dependency between environment and preference domains. Our approach is validated through simulations that demonstrate a significant performance improvement with respect to the baseline model.