Recommendation systems help users find the information, products and services they most want to find. Collaborative filtering is the method of making automatic predictions about the interest of a user by collecting interest information from many users, which has been very successful recommendation technique for recommendation systems in both research and practice. However, the traditional collaborative filtering is slow to detect the interest of a user changing with time as a case of user behavior and to adapt the changes, because the traditional collaborative filtering uses Pearson's correlation coefficient between users with the numerous values of property. In this paper, we apply the wavelet analysis to collaborative filtering in order to reveal the trends hidden in the interest of a user and propose the wavelet-based collaborative filtering for adapting changes in user behavior. The results of the performance evaluation show that the proposed wavelet-based collaborative filtering makes the improvement in the personalized recommendations.