Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding of recommender systems. Evaluation metrics continue to be refined but we still need to account for the relative contributions of the various knowledge elements that play a part in the recommendation process. In this paper, we make a fine-grained analysis of a successful approach in the area of case-based recommendation, providing an ablation study of similarity knowledge and similarity metric contributions to improved system performance. We gauge the strengths and weaknesses of knowledge components and discuss future work as well as implications for research in the area.