1. A user-based collaborative filtering recommendation method comprising: a first step of calculating the similarity between a user A and another user B; a second step of amending the calculated similarity by using each user preference information entropy of the user A and the user B; a third step of forming a nearest neighbor list of the user A based on the amended similarity; and a fourth step of predicting the preference of the user A by using the preference of the users in the formed nearest neighbor list, wherein the second step of amending the similarity comprises the steps of: obtaining the information entropy H(A) of the user A and the information entropy H(B) of the user B; obtaining a difference value between the information entropy H(A) of the user A and the information entropy H(B) of the user B; obtaining a reciprocal number of the value obtained by adding 1 to the difference value between the H(A) and H(B); and obtaining the value obtained by multiplying the similarity between the user B and the user A calculated in the similarity calculating step by the reciprocal number of the value obtained by adding 1 to the difference value between H(A) and H(B) as amended similarity.