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Collaborative filtering (CF) systems help address information overload, by using the preferences of users in a community to make personal recommendations for other users. The widespread use of these systems has exposed some well-known limitations, such as sparsity, scalability, and cold-start, which can lead to poor recommendations. During the last years, a great number of works have focused on the improvement of CF, but they do not solve all its problems efficiently. In this article, we present a new approach that applies semantic similarity fusion as well as biclustering to alleviate the aforementioned problems. The experimental results verify the effectiveness and efficiency of our approach over the benchmark CF methods. [ABSTRACT FROM AUTHOR]