Information Sciences: an International Journal
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
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Personalized recommendation systems can help people to find interesting things and they are widely used in our life. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of source data set is the major reason causing the poor quality. Aiming at the problem of data sparsity for collaborative filtering, a novel rough set and fuzzy clustering based collaborative filtering recommendation is proposed. This algorithm addresses the issue by automatically filling vacant ratings based on rough set theory, and uses the fuzzy clustering technology to compute user similarity and form nearest neighborhood, and then generates recommendations. The experiment results argue that the algorithm efficiently improves sparsity of rating data, and promises to make recommendations more accurately than conventional collaborative filtering.