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Collaborative filtering has been very successful in both research and applications. Current collaborative filtering based on clustering compute the whole set of items during the process of clustering or selecting nearest-neighbors, because the researchers believed if users have similar preferences on some of items, they will have the similar preferences on other items. But we think that users have...
Collaborative filtering has two methodologies: user based one and item based one. The former uses the similarity between users to predict, while the latter uses the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem: data sparsity. In this paper, we propose a hybrid approach to overcome the problem. We define a similarity...
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