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Recommender systems research combines techniques from user modeling and information filtering in order to build search systems that are better able to respond to the preferences of individual users during the search for a particular item or product. Collaborative recommenders leverage the preferences of communities of similar users in order to guide the search for relevant items. The success of collaborative recommendation has always been restrained by the so-called sparsity problem, in which a lack of available user similarity knowledge ultimately limits the formation of high-quality user communities and has a subsequent impact on recommender accuracy. This article presents an approach to addressing the sparsity problem by describing and evaluating how implicit similarity knowledge can be discovered and exploited using data-mining techniques and an approach to recommendation that is inspired by case-based reasoning research.

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.

Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying Singular Value Decomposition to reduce the dimensionality of the data, our system makes use of a dynamic Artificial Neural Network architecture with boosted learning to predict user ratings. Furthermore we use the concept of k-separability to deal with the resulting noisy data, a methodology not yet tested in Recommender Systems. The combination of these techniques applied to the MovieLens datasets seems to yield promising results.