Collaborative filtering algorithms have been successfully applied in personalized recommendation. However, it is hard for traditional filtering algorithms to make sure that the accuracy and reliability of the nearest neighbors set is good enough because they ignore the impact of time factor. Even though there are a number of improved collaborative filtering algorithms, they cannot integrate time factor and user scores in their calculation. We propose a new algorithm based on time factor and our original work, which introduces time factor into user score prediction and user similarity calculation and assigns each item a dynamic weight by synthesizing time and user similarity. We finally obtain a Top-N set by filling the user-item matrix with user prediction scores and secondary calculating user similarity matrix. Experiments prove that the improved algorithm can enhance  recommendation accuracy and quality.