摘要：Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommendation systems in recent years, the difficulties of the extreme sparsity of user rating data have become more and more severe. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalization recommendation algorithm based on rough set which is proposed, The algorithm refine the user ratings data with dimensionality reduction, then uses a new similarity measure to find the target users’ neighbors, then generates recommendations. To prove our algorithm’s effectiveness, the authors conduct experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.
