Choosing from a set of available options often requires resolution of trade-offs but it can be unfeasible for humans to carefully evaluate each option of a large set due to the required time and cognitive effort. Consequently, they are often unsatisfied with their choices. Software systems can support human decision making or even automate this process, but there are many challenges associated with the provision of such support. In this thesis we deal in particular with three of them: (i) how to represent user preferences; (ii) how to reason about preferences and make decisions; and (iii) how to justify such decisions. Different approaches have been proposed for representing and reasoning about qualitative preferences, but they address a restricted set of preference types, and therefore are not able to process preferences provided by users in many realistic scenarios. This thesis provides three main contributions. First, we introduce a new preference metamodel founded on a study of how humans express preferences, allowing the representation of high-level preferences. Second, we propose an automated decision making technique, which chooses an option from a set available based on preferences expressed in a language based on our metamodel, exploiting natural-language terms. Our technique goes beyond the provided preferences to make a decision with the incorporation of psychology principles, which concern how humans make decisions, as the provided preferences are typically not enough to resolve trade-offs among available options. Third, we present an explanation generation technique, which uses models built by our decision making technique to justify choices, and follows guidelines and patterns that we derived from a study of choice explanation. A user study was performed to evaluate our approach, which shows that (i) our preference language is adequate for users to express their preferences, (ii) our decision making technique makes choices that users consider as having good quality, and (iii) the provided explanations allow users to understand why the choice was made and improves the confidence in the decision.