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Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. However, most existing collaborative filtering based recommendation systems suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. So, a new collaborative filtering recommendation based on fuzzy similar-priority comparison and fuzzy clustering is presented. This method uses the fuzzy similar-priority comparison to compute user similarity and uses the fuzzy clustering technology to form nearest neighborhood, and then generates recommendations. The experimental results show that the presented algorithm can improve the performance of systems in the recommendation quality.