Abstract Collaborative filtering by the learning manifold alignments provides a new way for cross system personalization. It uses similarity between users to compute reconstruction weights. However, inaccurate similarity often leads to inaccurate weights and poor recommendation quality. By combining the topology and geometry structures of data set to calculate weight matrix, an improved collaborative filtering algorithm based on manifold alignments is proposed. The proposed algorithm removes the effect of similarity error on recommendation quality effectively. The experimental result indicates that the improved algorithm has better recommendation quality than that of the original algorithm.
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2009, Vol. 22 
