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Recommender systems combine research from user profiling, information filtering and artificial intelligence to provide users with more intelligent information access. They have proven to be useful in a range of Internet and e-commerce applications. Recent research has shown that a content-based (or case-based) perspective on collaborative filtering for recommendation can provide significant benefits in decision support accuracy over traditional collaborative techniques, particularly as dataset sparsity increases. These benefits derive both from the use of more sophisticated case-based similarity metrics and from the proactive maintenance of item similarity knowledge using data mining. This article presents a natural next step in this ongoing research to improve the quality of recommender systems by validating these findings in the context of more complex models of collaborative filtering, as well as by demonstrating that such techniques also preserve recommendation diversity, one of the key issues affecting traditional recommender systems.

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.