Recommender systems bring together ideas from information retrieval and filtering, user profiling, and machine learning in an attempt to provide users with more proactive and personalized information systems. Forwarded as a response to the information overload problem, recommender systems have enjoyed considerable theoretical and practical successes, with a range of core techniques and a compelling array of evaluation studies to demonstrate success in many real-world domains. That said, there is much yet to understand about the strengths and weaknesses of recommender systems technologies and in this article, we make a fine-grained analysis of a successful case-based recommendation approach. We describe a detailed, fine-grained ablation study of similarity knowledge and similarity metric contributions to improved system performance. In particular, we extend our earlier analyses to examine how measures of interestingness can be used to identify and analyse relative contributions of segments of similarity knowledge. We gauge the strengths and weaknesses of knowledge components and discuss future work as well as implications for research in the area.