The performance of recommender systems is commonly characterized by metrics such as precision and recall. However, these metrics can only provide a coarse characterization of the system, as they offer limited intuition and insights on potential system anomalies, and may fail to provide a developer with an understanding of the strengths and weaknesses of a recommendation algorithm. In this work, we start to describe a model of recommender systems that defines a space of properties. We begin exploring this space by defining templates that relate to the properties of coverage and diversity, and we demonstrate how instantiated characteristics offer complementary insights to precision and recall.