We present a multi-attribute decision model and a method for explaining the decisions it recommends based on an argumentative reformulation of the model. Specifically, (i) we define a notion of best (i.e., minimally redundant) decisions amounting to achieving as many goals as possible and exhibiting as few redundant attributes as possible, and (ii) we generate explanations for why a decision is best or better than or as good as another, using a mapping between the given decision model and an argumentation framework, such that best decisions correspond to admissible sets of arguments. Concretely, natural language explanations are generated automatically from dispute trees sanctioning the admissibility of arguments. Throughout, we illustrate the power of our approach within a legal reasoning setting, where best decisions amount to past cases that are most similar to a given new, open case. Finally, we conduct an empirical evaluation of our method with legal practitioners, confirming that our method is effective for the choice of most similar past cases and helpful to understand automatically generated recommendations.