Automated decision making often requires solving difficult and primarily NP-hard problems. In many AI applications (e.g., planning, robotics, recommender systems, etc), users can assist decision making by specifying their preferences over some domain of interest. To take preferences into account, we take a model-theoretic approach to both computations and preferences. Computational problems are characterized as model expansion, that is the logical task of expanding an input structure to satisfy a specification. The uniformity of the model-theoretic approach allows us to link preferences and computations by introducing a framework of a prioritized model expansion. The main technical contribution is an analysis of the impact of preferences on the computational complexity of model expansion. We also study an application of our framework for the case where some information about preferences is missing. Finally, we discuss how prioritized model expansion can be related to other preference-based declarative programming paradigms.